https://iasc-isi.org/dssv2019/

Abstracts
SATELLITE CONFERENCE OF THE 62nd WORLD STATISTICS CONGRESS

13-15 August 2019
Doshisha Univ., Kyoto, Japan

Book of Abstracts
Data Science, Statistics & Visualisation

DSSV 201913-15 August 2019
Imadegawa Campus, Doshisha University, Kyoto, Japan

https://iasc-isi.org/dssv2019/

Sponsors:
International Association for Statistical Computing (IASC)
The Japan Statistical Society
The ISI Tokyo Memorial FundThe Institute of Statistical Mathematics
Japanese Society of Computational Statistics
Japanese Federations of Statistical Science Associations

Preface

After successful first and second conferences, DSSV (Data Science, Statistics & Visualization) 2017 held in Lisbon, Portugal, and DSSV 2018 held in Vienna, Austria, the third
conference DSSV 2019 in this series takes place at Doshisha University in Kyoto city,
Japan, from August 13-15, 2019. It is organized under the auspices of the International
Association for Statistical Computing, as a satellite conference of the 62nd ISI World
Statistics Congress.
DSSV 2019 is cosponsored by the Institute of Statistical Mathematics in Japan, theJapan Statistical Society, and the Japanese Society of Computational Statistics. DSSV2019 is also patronaged by the Japanese Federation of Statistical Science Associations. In
particular, DSSV 2019 is financially supported by the ISI Tokyo Memorial fund from theJapan Statistical Society. DSSV 2019 is also financially supported by the grant from theInstitute of Statistical Mathematics in Japan.
DSSV 2019 brings together researchers and practitioners in the interplay between data science, statistics and visualization. This meeting creates a forum to discuss recent progressand emerging ideas in these diﬀerent disciplines and encourage informal contacts and discussions among all the participants. The conference includes contributions to practicalaspects of data science, statistics and visualisation, and in particular, those which arelinking and integrating these subject areas. Presentations are thus oriented towards avery wide scientific audiences, and will cover topics such as machine learning, bioinformatics, statistically visualizing human brain activities, business and industrial statistics,
sparse estimation, advanced computing, and other important themes.
DSSV 2019 features keynote addresses by Di Cook (Monash University, Australia), KwanLiu Ma (University of California, USA), and Richard Samworth (University of Cambridge,UK), as well as invited lectures by Genevera Allen (Rice University, USA), Yongdai Kim(Seoul National University, Korea), Hidetoshi Shimodaira (Kyoto University, Japan), andHuixia Judy Wang (George Washington University, USA). The conference also includes
nine invited talk sessions, in collaboration with the International Society for Business andIndustrial Statistics, the Japan Statistical Society, the Japanese Society of Computational
Statistics, Academia Sinica in Taiwan, Doshisha University, and the groups studying the
subjects to be emphasized in DSSV.
This book contains some practical information for the conference, the scientific programifor DSSV 2019, and the abstracts of all the presentations.
We, the local organizers, wish to express our gratitude to all persons and institutions
who make DSSV 2019 possible: the colleagues from the Scientific Program Committee,
the advisers for the local organizers, the keynote and invited speakers, the organizers ofinvited talk sessions and their speakers, the sponsors, and all the people who contributeto the scientific program. We would like to thank all the participants for joining DSSV
2019, and look forward to an exciting conference.
Kyoto, August 2019
Kohei Adachi
Hiroshi Yadohisa
Kosuke Okusa
Fumitake Sakaori
Wataru Sakamoto
Makoto Tomita
Hisayuki Hara
Hidetoshi Matsui
Michio YamamotoiiConference Venue
The conference takes place at “Ryoshinkan” (RY: 良心館) on Imadegawa Campus,
Doshisha University.
The address is: Karasuma-higashi-iru, Imadegawa-dori, Kamigyo-ku, Kyoto-shi 602-8580
As shown on the map (see the next page), the “Imadegawa Campus” can be reached by
the Karasuma subway line (“Imagedawa” station).

Registration Office and Lecture Rooms:
The registration desk is located at “Lucent Plaza” on the 1st floor, see map on the next
page. The main lecture room is RYB2, and further lecture rooms are RY101, RY102,
RY105 and RY105 (on the 1st floor). The registration desk will be open on August 13,
from 8:15 to 11:00. Late registration will also be possible on the other conference days
from 9:00-15:00 at the same place. At the registration you can receive your confirmationof payment, Book of Abstracts, and your ordered tickets for the conference dinner.

Guidelines for Speakers:
All rooms are equipped with a notebook and a projector. Please, bring your presentationas PDF file (recommended) or PPT file on a USB memory-stick, or bring your notebook
with an HDMI/VGA port and contact the person responsible for the notebook before thesession starts. Session Chairs are responsible for keeping the schedule.

Guidelines for Poster Presenters:
Poster walls will be available and located at “Lucent Plaza” on the 1st floor. The available
size for one poster is 841mm × 1189mm (portrait), for format A0 “high”. Please, fix yourposter at the beginning of the conference, and leave it until the end of the conference.

WLAN and Computer Room:You can use your eduroam account, if provided by your institution.
Alternatively, you can use the guest wifi DO-NET1x with ID:guest001, Pass:Dssv2019.

Lunch (Included) is located at the lounge area on the first floor in the “Ryoshinkan”
building. It is self-service, and no payment is needed. Alternatively, you will find many
restaurants and grocery stores near the campus.

iii

iv

Scientific Program CommitteePeter Filzmoser, TU Wien, Austria (Chair)
Kohei Adachi, Osaka University, JapanDavid Banks, Duke University, USA
Patrick Groenen, Erasmus University Rotterdam, The Netherlands
Helwig Hauser, University of Bergen, Norway
Xuming He, University of Michigan, USA
Eric Postma, Tilburg University, The Netherlands

Local Organizing Committee
Kohei Adachi, Osaka University, Japan (Chair)
Hiroshi Yadohisa, Doshisha University, Japan (Vice Chair)
Kosuke Okusa, Kyushu University, Japan
Fumitake Sakaori, Chuo University, Japan
Wataru Sakamoto, Okayama University, Japan
Makoto Tomita, Yokohama City University, Japan
Hisayuki Hara, Doshisha University, Japan
Hidetoshi Matsui, Shiga University, Japan
Michio Yamamoto, Okayama University, Japan

Advisers for Local Organizing Committee
Yasuhiro Omori, Tokyo University, Japan
Yutaka Kano, Osaka University, Japan
Koji Kurihara, Okayama University, Japan
Hiroshi Saigo, Waseda University, Japan
Yuichi Mori, Okayama University of Science, Japanv

Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

i

Conference Venue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

iii

Program Overview: Tuesday, 13 August 2019 . . . . . . . . . . . . . . . . . . .

1

Program Overview: Wednesday, 14 August 2019 . . . . . . . . . . . . . . . . . . 10
Program Overview: Thursday, 15 August 2019 . . . . . . . . . . . . . . . . . . . 15
Abstracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Index of Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

Program Overview
Tuesday, 13 August 2019

Registration 8:15 – 9:00
Opening Ceremony

9:00 – 9:30
Room: RYB2

Keynote Presentation

9:30 – 10:30
Tuesday 13th
Room: RYB2

Chair: Jean-Michel Poggi, University Paris-Sud & University Paris
Descartes, France

Timothy I. Cannings, Yingying Fan, Richard J. Samworth
Classification with Imperfect Training Labels ··············································· p.17

Coffee Break 10:30 – 11:00
Jpn Comp Stat Soc Session: Recent Advances in Biostatistics and Bioinformatics
Invited Topic Session: ITS_1
Organizer/Chair: Takayuki Abe, Yokohama City University, Japan

11:00 – 12:30
Tuesday 13th
Room: RY101

Takayuki Abe
Recent Applications of Biostatistical Methods in Medicine
11:00 – 11:30 ···················································································· p.18
Zachary Skrivanek, Hiroshi Nishiyama
SafetyGraphics R Library An Open-Source and Clinical Workflow for Assessing Hepatoxicity in Clinical Trials
11:30 – 12:00 ···················································································· p.19
Yusuke Matsui
Toward Achieving Precision Health Driven by Biomedical Informatics with Data Science
12:00 – 12:30 ···················································································· p.20

Clustering
Contributed Paper Session: CPS_1
Chair: Michio Yamamoto, Okayama University, Japan

11:00 – 12:30
Tuesday 13th
Room: RY102

Michel van de Velden, Alfonso Iodice D’Enza, Angelos Markos
On Two Distance-based Clustering Methods for Mixed Data
11:00 – 11:20 ···················································································· p.21
Jean-Michel Poggi, Avner Bar-Hen, Servane Gey
Spatial CART Classification Trees
11:20 – 11:40 ···················································································· p.22
– 1–

Shang-Ying Shiu, Ting-Li Chen
Functional Subspace Clustering: a Self-updating Process Approach
11:40 – 12:00 ···················································································· p.23
Yaeji Lim, Hee-Seok Oh
A Generalization of Functional Clustering for Discrete Multivariate Longitudinal Data
12:00 – 12:20 ···················································································· p.24

Visualization 1
Contributed Paper Session: CPS_2
Chair: Ginina Elena, VRVis Research Center for Virtual Reality and
Visualization, Austria

11:00 – 12:30
Tuesday 13th
Room: RY105

Szu-Han Lin, Ting-Li Chen, I-Ping Tu
Random Partition t-SNE
11:00 – 11:20 ···················································································· p.25
Junji Nakano, Nobuo Shimizu, Yoshikazu Yamamoto
A Visualization of Aggregated Symbolic Data
11:20 – 11:40 ···················································································· p.26
Shizue Izumi, Tetsuji Tonda, Noriyuki Kawano, Kenichi Satoh
Statistical Model Based Text Mining and Visualization: Application to LongitudinallyObserved Data
11:40 – 12:00 ···················································································· p.27
Masahiro Mizuta
Visualization of Eﬀects on Radiotherapy
12:00 – 12:20 ···················································································· p.28

Sports and Behavioral Data Science
Contributed Paper Session: CPS_3
Chair: Yoshiro Yamamoto, Tokai University, Japan

11:00 – 12:30
Tuesday 13th
Room: RY106

Kazuki Konda, Yoshiro Yamamoto
The Impact of Temperature on Nippon Professional Baseball
11:00 – 11:20 ···················································································· p.29
Tsuneshi Obata, Shizue Izumi
Assessing the Eﬀects of Time-dependent Match Environments on the Soccer Team Performances
11:20 – 11:40 ···················································································· p.30
Takuma Narizuka, Yoshihiro Yamazaki
Characterization of the Formation Structure in Team Sports Using Delaunay Triangulation
11:40 – 12:00 ···················································································· p.31

– 2–

Wataru Takano
Statistical Approach Toward Automatic Annotation of Human Behavior
12:00 – 12:20 ···················································································· p.32

Lunch Break 12:30 – 13:40
Short Oral Presentations
Chair: Kensuke Okada, University of Tokyo, Japan

13:40 – 14:20
Tuesday 13th
Room: RY101

Chen-An TsaiA Clustering-based Approach for Selecting Core Collection
13:40 – 13:45 ···················································································· p.33
Yuki Morioka, Kensuke Tanioka, Hiroshi Yadohisa
Mixtures of Probabilistic Principal Component Analysis for NMAR Missing Data
13:45 – 13:50 ···················································································· p.34
Ippei Takasawa, Kensuke Tanioka, Hiroshi Yadohisa
Fuzzy Clusterwise Generalized Structured Component Analysis for Mixed Data
13:50 – 13:55 ···················································································· p.35
Masamichi ito, Kohei Adachi
Uniqueness-dissociated Factor Analysis Modified for Providing Sparse Loadings and Extended Common Factors
13:55 – 14:00 ···················································································· p.36
Yoshihiro Hirose
LARS-type Estimation Procedure for Generalized Linear Models
14:00 – 14:05 ···················································································· p.37
Danilo Guimarães Gonçalves, Yoshihiro Hirose
A Geometric Estimation Method for Autoregressive Models
14:05 – 14:10 ···················································································· p.38

Statistical Image Processing
Contributed Paper Session: CPS_4
Chair: Ian McKeague, Columbia University, USA

13:40 – 14:20
Tuesday 13th
Room: RY102

Shuen-Lin Jeng
Clustering Partially Masked Images from Wafer Bin Maps
13:40 – 14:00 ···················································································· p.39
Shao-Hsuan Wang, Yi-Ching Yao, Wei-Hau Chang, I-Ping Tu
Analyzing Model Bias in Cryo-EM Single-Particle Image Processing
14:00 – 14:20 ···················································································· p.40

– 3–

Nonmetric Principal Component Anaysis
Contributed Paper Session: CPS_5
Chair: Niel Le Roux, Stellenbosch University, South Africa

13:40 – 14:20
Tuesday 13th
Room: RY105

Naomichi Makino
Joint Oblique Rotation in Multiple Correspondence Analysis and Its Properties
13:40 – 14:00 ···················································································· p.41
Yuichi Mori, Hiroko Katayama, Takatsugu Yoshioka, Masahiro Kuroda, Masaya Iizuka
Some Applications in Multivariate Methods with Alternating Least Squares
14:00 – 14:20 ···················································································· p.42

Longitudinal Data Science
Contributed Paper Session: CPS_6
Chair: Hidetoshi Matsui, Shiga University

13:40 – 14:20
Tuesday 13th
Room: RY106

M. Fariz Fadillah Mardianto, Sri Haryatmi Kartiko, Herni Utami
Trigonometric Forms of Fourier Series Estimator in Nonparametric Regression for Longitudinal Data
13:40 – 14:00 ···················································································· p.43
Dedi Rosadi, Muslim, Agus Sihabuddin
Forecasting the Term Structure of Yields Bond
14:00 – 14:20 ···················································································· p.44

Poster

14:30 – 15:50
Tuesday 13th
Room: Lucent plaza

Kuo-Jung Lee, Sheng-Mao Chang
Application of Bayesian Spatiotemporal Varying Coeﬃcients Model to the Mortality from
Ischemic Stroke ·················································································· p.45
Sho Hashimoto, Taiki Shimozono, Noriko Nagata
Bayesian Estimation of Impression Discriminant Ability in Paired Comparison ······p.46
Aldy Pradana, Zalfa Nur Amalia, Ismiyatun Hanifah, M. Fariz Fadillah MardiantoStatistical Analysis for Livestock Sectors In Indonesia Based on Factors That Related to
The Development of Industrial Revolution ·················································· p.47
Vemmie Nastiti Lestari, Subanar
K-Means Clustering On Text Data ··························································· p.48
Kuan-Hui Wu, Huey-Miin Hsueh
Chinese Word Segmentation by Considering Dependence Between Adjacent Words ·· p.49
Chen-An TsaiA Clustering-based Approach for Selecting Core Collection ·······························p.33– 4–Very Basuki, Nurani Aprilia
Growing Bigger and More Accurate with GSBPM Part Five ····························· p.50
Isni Sukrianah, Aulia Dini, Rahmiati Rahim
Establishment Approach Survey to Update Household Approach Survey (part One) [HES]
p.51
Yunita Wulan Sari, Gunardi
Pricing of Agricultural Insurance Premium in Indonesia with The Option Approach p.52
Tasuku Yamasaki
Comparison Between Estimation Methods for Undirected Networks ·····················p.53
Sri Haryatmi Kartiko, M. Fariz Fadillah MardiantoHazard Ratio Function Based on Kernel Estimator in Modeling Stunting Toddler’s Height
p.54
Reynaldi Aries Ariyanto, Raka Andriawan, Devayanti Anugerahing Husada, M. Fariz
Fadillah Mardianto
Two Forms of Complete Fourier Series Estimator to Predict Contribution for "Suroboyo"
Bus Payment System ··········································································· p.55
Ya-Mei Chang
Investigating the Dependence of Two Diﬀerent Species from Presence-absence Maps p.56
Tsai-Yu Lin
Interval Estimation for Nonconformance Proportion of Air Quality Index ·············p.57
Masahiro Kobayashi, Kazuho Watanabe
Unbiased Estimation Equation Under f-separable Extension of Squared and Itakura-Saito
Distances ························································································· p.58
Hung Hung
A Robust RUV-testing Procedure via Gamma-divergence ································· p.59
Sheng-Hsuan Lin
Generalized Interventional Approach for Causal Mediation Analysis with Causally Ordered
Multiple Mediators ·············································································· p.60
Yunchan Chi
Confidence Bands for Quantile Residual Life Function with Clustered Survival Data p.61
Yuki Morioka, Kensuke Tanioka, Hiroshi Yadohisa
Mixtures of Probabilistic Principal Component Analysis for NMAR Missing Data ·· p.34
Raka Andriawan, Devayanti Anugerahing Husada, Ismiyatun Hanifah, M. Fariz Fadillah
Mardianto
Mapping of The PM2.5 Sources Eﬀect to Respiration and Cardiac Health Based on Geographically Weighted Regression in Indonesia ··············································· p.62

– 5–

Herni Utami
Modelling of Corn Productivity in Central Java, In- Donesia Using Hyper-Local Geographically Weighted Regression ····································································· p.63
Jungteak Jang, Kee-Hoon Kang
Local Linear Regression Methods for Interval-valued Data ································p.64
Rosyida Widadina Ulya, Almira Sophie Syamsudin, M. Fariz Fadillah Mardianto
Structural Equation Modeling for Society Satisfaction Analysis of The E-Toll System inIndonesia ························································································· p.65
Zalfa Nur Amalia, Rosyida Widadina Ulya, Disty Ridha Hastuti, M. Fariz Fadillah Mardianto
Structural Equation Modeling in Motivation Analysis for Millennial Participation Related
to General Elections in Indonesia ·····························································p.66
Ippei Takasawa, Kensuke Tanioka, Hiroshi Yadohisa
Fuzzy Clusterwise Generalized Structured Component Analysis for Mixed Data ·······p.35
Shuji Ando
Variable Selection in Propensity Score Using Adaptive Bridge for Confounding Adjustment
p.67
Yasuyuki Kobayashi
New Sparse Modeling of Sample Mahalanobis Distance ··································· p.68
Masamichi ito, Kohei Adachi
Uniqueness-dissociated Factor Analysis Modified for Providing Sparse Loadings and Extended Common Factors ········································································p.36
Yoshihiro Hirose
LARS-type Estimation Procedure for Generalized Linear Models ························ p.37
Rezki M. Sabil, Ray Sastri
Spatial Model For The Prevalence of Leprosy In South Sulawesi, Indonesia ··········· p.69
Chi-Wei Lai, Hsin-Cheng Huang, Sheng-Li Tzeng
Spatial Modeling of Ground-Level PM2.5 in Taiwan Based on Two Types of Data ··· p.70
Longcheen Huwang, Jian-Chi Lin, Li-Wei Lin
A Spatial Rank-based EWMA Chart for Monitoring Linear Profiles ···················· p.71
Tomoya Ohyama, Mamoru Amemiya
Crime Prediction of Street Harassment ······················································ p.72
Di Asih I Maruddani, Abdurakhman
Multi Compound Option for Bond Performance with Jump Diﬀusion Process ········ p.73
D. Danardono
Visual Analytics of a Large Longitudinal Survey Data ···································· p.74
– 6–

Hayafumi Watanabe
Statistical Modeling of Logarithmic Diﬀusions in Word Counts Time Series in Nation-wide
Language Data Sets ············································································· p.75
Danilo Guimarães Gonçalves, Yoshihiro Hirose
A Geometric Estimation Method for Autoregressive Models ······························ p.38

Biometrics
Contributed Paper Session: CPS_7
Chair: Michael G. Schimek, Medical University of Graz, Austria

14:30 – 15:50
Tuesday 13th
Room: RY102

Rizky Reza Fauzi, Yoshihiko Maesono
Mean Residual Life Function Estimators for Nonnegative Data by Logarithmic Transformation
14:30 – 14:50 ···················································································· p.76
Takanori Yoshikawa, Yoshimichi Ochi, Masashi Goto
Prediction of Model Mean Using Double Power Additive Transformation
14:50 – 15:10 ···················································································· p.77
Tsung-Shan Tsou
Inference About Binocular Sensitivity and Specificity of Screening Tests for Paired Organs
15:10 – 15:30 ···················································································· p.78

Dimension Reduction
Contributed Paper Session: CPS_8
Chair: Masahiro Mizuta, Hokkaido University, Japan

14:30 – 15:50
Tuesday 13th
Room: RY105

Jun Tsuchida, Hiroshi Yadohisa
Principal Component Analysis Using the Gini Coeﬃcient Penalty Function
14:30 – 14:50 ···················································································· p.79
Niel Le Roux, Ruan Rossouw, Roelof Coetzer
Implementing a Biplot Based Multivariate Data Driven Industrial Performance Index
14:50 – 15:10 ···················································································· p.80
Inge Koch
Cross-Validation Error in Classification with Dimension Reduction and Applications toProteomics Mass Spectrometry Imaging
15:10 – 15:30 ···················································································· p.81
Heng-Hui Lue
Suﬃcient Reduction in Large Ensembles of Time Series Analysis
15:30 – 15:50 ···················································································· p.82

Coffee Break 15:50 – 16:20– 7–

Statistical Signal Analysis and Estimation
Invited Topic Session: ITS_2
Chair: Toshinari Kamakura, Chuo University, Japan

16:20 – 17:20
Tuesday 13th
Room: RY101

Ian McKeague
Nonparametric Inference for Activity Profiles from Wearable Devices
16:20 – 16:50 ···················································································· p.83
Michael G. Schimek, Luca Vitale
Signal Estimation from Large Ranking Data Sets: Challenges and Strategies
16:50 – 17:20 ···················································································· p.84

Statistical Computing
Contributed Paper Session: CPS_9
Chair: Yoshiyasu Tamura, Institiue of Statistical Mathematics, Japan

16:20 – 17:20
Tuesday 13th
Room: RY102

Yuki Takara, Shingo Takagi
Measuring Unobserved Cultural Eﬀects on Music Trade: Grouping Interactive Fixed Effects in Sample Selection Models
16:20 – 16:40 ···················································································· p.85
Jim Harner, Mark Lilback
From Docker to Kubernetes: Scaling Machine Learning Algorithms
16:40 – 17:00 ···················································································· p.86
Atsushi Nitanda, Taiji Suzuki
Exponential Convergence of Stochastic Gradient Descent for Binary Classification Problems
17:00 – 17:20 ···················································································· p.87

Bayesian Analysis
Contributed Paper Session: CPS_10
Chair: Wataru Sakamoto, Okayama University, Japan

16:20 – 17:20
Tuesday 13th
Room: RY105

Tasuku Okui
A Bayesian Nonparametric Topic Model Using an Outcome Variable for Microbiome Data
16:20 – 16:40 ···················································································· p.88
Fanghong Zhang
Sample Sizes and Properties of Four Bayesian Designs for Proof-of-Concept Trials
16:40 – 17:00 ···················································································· p.89
Hiroyasu Abe
On the Determination of Number of Bases in Bayesian Nonnegative Matrix Factorization
Using the Marginal Likelihood
17:00 – 17:20 ···················································································· p.90

– 8–

Data Science for Human Learning
Contributed Paper Session: CPS_11
Chair: Kentaro Kato; Benesse Educational Research & Development
Institute, Japan

16:20 – 17:20
Tuesday 13th
Room: RY106

Kan Hishiyama, Kensuke Okada
Causal Eﬀect of Inquiry Teaching Using Generalized Propensity Score in Japan
16:20 – 16:40 ···················································································· p.91
Kazuya Fujita, Kensuke Okada
Computerized Adaptive Test That Incorporates Response Time for Personality Measurement
16:40 – 17:00 ···················································································· p.92
Hideo Hirose
Success/Failure Prediction Using A Large-scale Online Testing System with Its Learning
Analytics
17:00 – 17:20 ···················································································· p.93

Invited Talk
Chair: Wing Kam Fung, University of Hong Kong

17:25 – 18:10
Tuesday 13th
Room: RYB2

Hidetoshi ShimodairaMultiscale Bootstrap for Selective Inference with Applications to Model Selection ···· p.94

Welcome Reception 19:30 – 21:00
THE SODOH HIGASHIYAMA KYOTO
Address: 366 Yasaka Kami-machi, Yasakadori Shimokawara Higashi-iru,
Higashiyama-ku, Kyoto City
(18:15 – 19:30: Move by Courtesy Bus)– 9–

Wednesday, 14 August 2019
Keynote Presentation

9:00 – 10:00
Chair: Patrick J. F. Groenen, Erasmus Universiteit Rotterdam, Nether- Wednesday 14thlands
Room: RYB2
Dianne Cook, Shuofan Zhang
Human Vs Computer: In Visualising Data, Who Wins? ································· p.95

Invited Talk
Chair: Juergen Symanzik, Utah State University, USA

10:00 – 10:45
Wednesday 14th
Room: RYB2

Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering
Copula-Based Semiparametric Models for Spatio-Temporal Data ························ p.96

Coffee Break 10:45 – 11:15
ISBIS Session: Recent Advances in Business and Industrial 11:15 – 12:45
Statistics
Wednesday 14th
Invited Topic Session: ITS_3
Organizer/Chair: Paulo Canas Rodrigues, Federal University of Bahia,
BrazilRoom: RY101

Paulo Canas Rodrigues
Robust Singular Spectrum Analysis: Methodology and Application
11:15 – 11:45 ···················································································· p.97
Han Ye, James Luedtke, Haipeng Shen
Call Center Arrivals: When to Jointly Forecast Multiple Streams?
11:45 – 12:15 ···················································································· p.98

Acadimia Sinica - Doshisha Univ Session 1
Invited Topic Session: ITS_6
Chair: Chun-houh Chen, Acadimia Sinica, Taiwan

11:15 – 12:45
Wednesday 14th
Room: RY102

Grace S. Shieh, Yun-Chan Chi, Jan-Gowth Chang
Prediction of Cancer Patients’ Response to Drugs via Cell Lines and Machine Learning
11:15 – 11:45 ···················································································· p.99
Lu-Hung Chen, Ci-Ren Jiang
Predicting One-day-ahead Wind Power Capacity Factor via Functional Inverse Regression
11:45 – 12:15···················································································p.100
I-Ping Tu
Statistical Methods for Cryo-EM Image Analysis
12:15 – 12:45···················································································p.101– 10–

Spatial Statistics
Contributed Paper Session: CPS_12
Chair: Koji Kurihara, Okayama University, Japan

11:15 – 12:45
Wednesday 14th
Room: RY105

Yusuke Takemura, Fumio Ishioka, Koji Kurihara
Cluster Detection Based on Restricted Hierarchical Structure for Large Scale Data
11:15 – 11:35···················································································p.102
Shoji Kajinishi, Fumio Ishioka, Koji Kurihara
Hotspot Detection for Epidemiological Data Analysis Using R Shiny
11:35 – 11:55···················································································p.103
Takahito Shimada, Tomonori Saitoh, Yoshiko Yamane
Testing Diﬀerent Spatial and Temporal Units in Detecting and Visualizing Crime Concentration
11:55 – 12:15···················································································p.104
Aki-Hiro Sato, Naoki Makita, Shoki Nishimura
Stat On-demand for World Grid Square Statistics
12:15 – 12:35···················································································p.105

Deep Learning

11:15 – 12:45
Contributed Paper Session: CPS_13
Wednesday 14th
Chair: Taiji Suzuki, University of Tokyo & Center for Advanced Intel- Room: RY106
ligence Project, RIKEN, Japan
Elena Ginina, Denis Gracanin, Michael Beham, Rainer Splechtna, Kresimir Matkovic
A Tool for Deep Automatic Classification and Visual Analytics of Complex Spatiotemporal Data
11:15 – 11:35···················································································p.106
Guan-Hua Huang, Chih-Hsuan Lin
Machine Learning Classification of Functional Brain Imaging for Parkinson’s Disease
Stage Prediction
11:35 – 11:55···················································································p.107
Kenta Oono, Taiji Suzuki
Toward Understanding Expressive Power of Graph Convolutional Neural Networks
11:55 – 12:15···················································································p.108
Sho Sonoda
Generalized Kernel Quadrature for Synthesizing Neural Networks
12:15 – 12:35···················································································p.109

Lunch Break 12:45 – 13:55– 11–

Visualization of Brain Computations: from Neurons to Be- 13:55 – 15:25
haviorWednesday 14th
Invited Topic Session: ITS_4
Organizer/Chair: Nobuhiko Asakura, Osaka University, Japan

Room: RY101

Hideaki Shimazaki
Visualizing Dynamics of Cooperative Activities of Neurons for Neural Coding Studies
13:55 – 14:25···················································································p.110
Kenji Ogawa
Principles of FMRI Multivariate Analysis and Its Applications in Cognitive Neuroscience
14:25 – 14:55···················································································p.111
Tomoyuki Naito, Ryota Imai, Hiromichi Sato
Visualizing Mental Template of Attractive Facial Images of Humans and Deep Neural
Network
14:55 – 15:25···················································································p.112

Acadimia Sinica - Doshisha Univ Session 2
Invited Topic Session: ITS_7
Chair: Hiroshi Yadohisa, Doshisha University, Japan

13:55 – 15:25
Wednesday 14th
Room: RY102

Tso-Jung Yen, Yu-Min Yen
An Attention Algorithm for Solving Large Scale Structured l0 -norm Penalized Estimation
Problems
13:55 – 14:25···················································································p.113
Yuan-chin Ivan Chang
Statistical Methods and Active Learning
14:25 – 14:55···················································································p.114
Ting-Li Chen, Chih-Hsuan Wu, Wen-Shao Ho
Robust Linear Discriminant Analysis Based on γ-divergence
14:55 – 15:25···················································································p.115

Sparse Estimation
Contributed Paper Session: CPS_14
Chair: E. James Harner, West Virginia University, USA

13:55 – 15:25
Wednesday 14th
Room: RY105

Isamu Nagai
Shrinkage Estimator for the Precision Matrix and Its Optimization in Closed Form
13:55 – 14:15···················································································p.116
Toshio Honda
The De-biased Group Lasso Estimation for Varying Coeﬃcient Models
14:15 – 14:35···················································································p.117
Michael Weylandt, Genevera I. Allen
Sparse and Functional Principal Components Analysis
14:35 – 14:55···················································································p.118– 12–

Regression
Contributed Paper Session: CPS_15
Chair: Manabu Iwasaki, Yokohama City University, Japan

13:55 – 15:25
Wednesday 14th
Room: RY106

Toshiyuki Shimono
Interpreting Multiple Regression via an Ellipse Inscribed in a Square Extensible to Any
Finite Dimensionality
13:55 – 14:15···················································································p.119
Yuta Tanabe, Yuko Araki
Nonlinear Quantile Mixed-eﬀect Models Based on Basis Expansions
14:15 – 14:35···················································································p.120
Takemi Yanagimoto, Kosuke Ohkusa
Properties of the Ramp Function with Their Implications to an Activation Function in
Deep Neural Networks
14:35 – 14:55···················································································p.121
Olcay Arslan, Senay OzdemirRobust Penalized Empirical Likelihood Estimation Method for a Linear Regression Model
14:55 – 15:15···················································································p.122

Coffee Break 15:25 – 15:55
Invited Talk
Chair: Inge Koch, University of Western Australia, Australia

15:55 – 16:40
Wednesday 14th
Room: RYB2

Genevera Allen
Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization ·······················································································p.123

Jpn Stat Soc Session: Machine Learning and Statistics: 16:45 – 18:15
Their Great Achievements and Grand Challenges
Wednesday 14th
Invited Topic Session: ITS_5
Organizer/Chair: Takafumi Kanamori, Tokyo Institute of Technology,
RIKEN AIP, Japan

Room: RY101

Jun-ichiro Hirayama, Aapo Hyvärinen, Vesa Kiviniemi, Motoaki Kawanabe, Okito Yamashita
Characterizing Variability of Brain Connectivity with Constrained Principal Component
Analysis16:45 – 17:15···················································································p.124
Taiji Suzuki
Compression Based Bound for Non-compressed Deep Neural Network Models and Their
Data Adaptivity
17:15 – 17:45···················································································p.125– 13–

Kenji Fukumizu
Machine Learning Approach to Topological Data Analysis
17:45 – 18:15···················································································p.126

Acadimia Sinica - Doshisha Univ Session 3
Invited Topic Session: ITS_8
Chair: Hsin-Cheng Huang, Acadimia Sinica, Taiwan

16:45 – 18:15
Wednesday 14th
Room: RY102

Frederick Kin Hing Phoa
Designing Experiments with Unstructured Treatments for General Network Structures
16:45 – 17:15···················································································p.127
Yuejun Zheng
Reliability and Validity of the Public Opinion Poll in Japan
17:15 – 17:45···················································································p.128
Hisayuki Hara
Multiplicity Adjustment with Markov Property in Temporal and Spacial Epidemiology
17:45 – 18:15···················································································p.129

Visualization 2
Contributed Paper Session: CPS_16
Chair: Shizue Izumi, Shiga University, Japan

16:45 – 18:15
Wednesday 14th
Room: RY105

Masayuki Jimichi, Daisuke Miyamoto, Chika Saka, Shuichi Nagata
Exploratory Financial Big Data Analysis and Reproducible Research
16:45 – 17:05···················································································p.130
Chika Saka, Masayuki Jimichi
Visualization of Corporate Tax Avoidance and Value Added Distribution: Exploratory
Analysis of Financial Big Data
17:05 – 17:25···················································································p.131
Sheng-Hsuan Lin
Visualization and Identification of Agonistic Interaction: Hepatitis B and C Interaction
on Hepatocellular Carcinoma
17:25 – 17:45···················································································p.132
S.S.M. Silva, Denny Meyer, Madawa W. Jayawardana
Mixture Hidden Markov Models to Detect the Engagement Dynamics of Mhealth Participants
17:45 – 18:05···················································································p.133

Conference Dinner 19:30 – 21:30
TsuruseAddress: 451 Gojō Agaru, Shimozaimokucho, Shimogyo Ward, Kyoto Prefecture, Kyoto(18:15 – 19:30: Move by Courtesy Bus)
– 14–

Thursday, 15 August 2019
Data Science: Fintech, Health and Algorithm
Invited Topic Session: ITS_9
Organizer: Ying Chen, National University of Singapore Chair: Yuanchin Chang, Academia Sinica, Taiwan

9:00 – 10:30
Thursday 15th
Room: RY101

Ying Chen, Linlin Niu, Ray-Bing Chen, Qiang He
Sparse-Group Independent Component Analysis with Application to Yield Curves Prediction
9:00 – 9:30······················································································p.134
Cathy W.S. Chen, K. Khamthong
Bayesian Modelling Weekly Dengue Hemorrhagic Fever Counts
9:30 – 10:00 ···················································································· p.135
Patrick J.F. Groenen, Yoshikazu Terada, Mariko Takagishi, Tom van den BergA Fast MM Algorithm for Clusterpath
10:00 – 10:30···················································································p.136

Mathematical Statistics
Contributed Paper Session: CPS_17
Chai: Yoshihiko Maesono, Chuo University, Japan

9:30 – 10:30
Thursday 15th
Room: RY102

Shigekazu Nakagawa, Hiroki Hashiguchi, Yoko Ono
Asymptotic Distribution of Sample Correlation Coeﬃcient Under Elliptical Population
9:30 – 9:50······················································································p.137
Hiroki Watanabe, Masashi Hyodo, Takashi Seo
Inference on High-dimensional Mean Vectors Under Alternative Hypothesis
9:50 – 10:10 ···················································································· p.138
Kenichi Hayashi, Keiji Takai
A Model Selection Criterion for Missing Data with MAR Mechanism but NMAR for Its
Subsets
10:10 – 10:30···················································································p.139

Education and Philosophy for Data Science
Contributed Paper Session: CPS_18
Chair: Yoshizoe Yasuto, Aoyama Gakuin University, Japan

9:30 – 10:30
Thursday 15th
Room: RY105

Kazuyuki Koizumi, Yoko Ono, Masataka Taguri, Jinfang Wang
Data Science As Mode 2 Science: Yokohama City University
9:30 – 9:50······················································································p.140
Shizue Izumi
A Strategy of Data Science Education Related SDGs
9:50 – 10:10 ···················································································· p.141– 15–

Yoshiyuki Takeuchi
Philosophical Foundations of the Big Data Analysis and Data Driven Science
10:10 – 10:30···················································································p.142

Coffee Break 10:30 – 11:00
Invited Talk
Chair: Michel van de Velden, Erasmus Universiteit Rotterdam, Netherlands

11:00 – 11:45
Thursday 15th
Room: RYB2

Jongjune Jeaon, Yongdai Kim, Dongha Kim
Rank-consistency of the Generalized Bradley-Terry Model with Link Misspecificationp.143

Keynote Presentation
Chair: Kohei Adachi, Osaka University, Japan

11:50 – 12:50
Thursday 15th
Room: RYB2

Kwan-Liu Ma
Data-intensive Discovery and Storytelling Using Visualization ························· p.144

Closing Ceremony

12:50 – 13:05
Room: RYB2

– 16–

Keynote: Tuesday 13 (9:30-10:30) room RYB2, page: 1

Classification with Imperfect Training Labels
Timothy I. Canningsa , Yingying Fanb and Richard J. Samworthca University of Edinburgh, b University of Southern California, c University of Cambridge

Supervised classification is one of the fundamental problems in statistical learning. However, it is frequently the case that the class labels in the training data are inaccurate, either
due to a coding error, or because the true labels are diﬃcult or expensive to determine.
We will present both general theory to characterise the eﬀect of label noise on an arbitraryclassifier. We will then specialise to three popular approaches to classification, namely the
k-nearest neighbour classifier, support vector machines and linear discriminant analysis,
and show that, under stronger conditions, more detailed asymptotic properties may bederived. Our conclusions act as a counterpoint to much of the folklore in the computerscience/machine learning literature.

– 17–

ITS_1: Tuesday 13 (11:00-12:30) room RY101, page: 1

Recent applications of biostatistical methods in medicine
Takayuki Abea
a Yokohama City University, School of Data Science

Recently, new biostatistical methodologies have been applicable in clinical studies due
to some improvements in software packages and computational algorithms. Speciﬁcally,
Bayesian methodologies have been implemented in many software packages. When physicians interpret ﬁndings from clinical studies, visualization of results is also powerful tool.
Such biostatistical and visualization methodologies in medicine will be introduced in thistalk. Firstly, applications of network meta-analysis (NMA) [1] will be discussed. Although, traditional meta-analysis combines results from clinical studies in which twotreatments are compared, the NMA pooled some studies where multiple treatments arecompared by means of a Bayesian method. In the NMA, visualization of a network diagram and posterior probabilities of the ranking of treatments are useful to understandthe ﬁndings. It is also emphasized that NMA assumes the consistency of treatment eﬀect
across the studies to utilize results from indirect comparisons, not only those from direct
ones.
Next topic is developments of patients’ prognostic models in various therapeutic areas.
A guideline for the model development has been issued for its standardization [2]. In
actual developments of models using clinical registry data, missing data is inevitable.
Speciﬁcally, when developing multiple regression models, many patients can be excluded
from the analysis and it is a potential problem. Multiple imputation [3] is a valid method
to handle such missing data if missing mechanism is missing at random (MAR) [4]. Then,sensitivity analyses are indispensable to evaluate the robustness of the study results. Somesensitivity analyses which assume missing not at random (MNAR), including those of thevisualization, will be shown.
Keywords: Biostatistics, Missing data, Network meta-analysis.

References[1] D Mavridis and G Salanti (2013). A practical introduction to multivariate metaanalysis. Statistical Methods in Medical Research, 22, 133-158.
[2] AC Alba, T Agoritsas, M Walsh et al (2017). Discrimination and calibration ofclinical prediction models. JAMA, 318, 1377-1384.
[3] DB Rubin (1987). Multiple Imputation for Nonresponse in Surveys. New York: Wiley.
[4] RJA Little and DB Rubin (2002). Statistical analysis with missing data, 2 nd edition.
Wiley.

– 18–

ITS_1: Tuesday 13 (11:00-12:30) room RY101, page: 1

SafetyGraphics R Library An Open-Source and Clinical Workflow for Assessing Hepatoxicity in Clinical
TrialsZ. Skrivaneka and H. Nishiyamaba Eli Lilly and Company, b Eli Lilly Japan K. K

Tables, figures and listings are an inefficient way to understand data relative to interactive
data visual tools with automated analyses. The DIA-ASA Biopharm Safety Evaluation
Working Group is developing a series of novel interactive safety graphic tools to enhance
the ability to identify and evaluate safety signals. Each will be made available as an
open-source, non-proprietary application.
The first tool to be released is designed to explore cases of potential drug-induced hepatotoxicity based on the eDISH plot developed by FDA. Building upon the existing static
eDISH plot, the tool allows the user to dynamically adjust laboratory thresholds, modify
the time dimension for the occurrence of peak ALT/AST and bilirubin values, account for
the extent of alkaline phosphatase elevation, with filters for treatment, gender, race andage. Cases that appear in the potential Hy’s Law, Temple’s corollary and hyperbilirubinemia quadrants can be individually explored in further detail.
The tool is accompanied by a proposed workflow to guide the analytical steps supported
by references to the medical literature. This will allow scientists to more easily identify
safety signals and perform exploratory analyses.
Keywords: Safety evaluation, Interactive graphic tool.

– 19–

ITS_1: Tuesday 13 (11:00-12:30) room RY101, page: 1

Toward Achieving Precision Health Driven by Biomedical Informatics with Data Science
Yusuke Matsuia
a Graduate school of medicine, Nagoya university

Recent advances in bioinformatics, including next-generation sequencers and IoT wearable
technologies, are enabling the acquisition of all kinds of human health and medical data,from the molecular level, including the genome, to the cellular, tissue, individual, and evensocial levels. Data science is growing as an interdisciplinary field that plays a central rolein the field of precision health care, and is expected to become a data-driven predictive
medicine by integrating all data from a comprehensive perspective. In this talk, I would
like to deepen the discussion on the data analysis methods required for precision healthresearch from the viewpoint of molecular omics data and neuro activity data.
One of the important issues in molecular omics data analysis is to accelerate the scientific discovery of new life mechanisms through the development of data-driven modelingby translating new hypotheses into mathematical models utilizing high-dimensional andhierarchical multi-omics data. In particular, it is important to focus the scope to a scalethat can be validated by experimental and clinical medicine, and to make the visualizationeasy to interpret. So far, many sparse high-dimensional data analysis methods have beendeveloped as “comprehensive” analysis methods focusing on molecular target screening.
On the other hand, analysis methods for modeling complex interrelationships such as
AND/OR such as synergy eﬀects ”with detail” still remain as a problem, and methodologies such as infinite statistical testing1 and logic regression models2 have been developed.
In addition, the application of Bayesian statistical models such as topic models is also
advancing as a technique capable of flexible modeling.
The analysis of neuro activity data requires not only qualitative evaluation of the firing
and their timing of neuro activity using multidimensional time-continuous data such as
electromyography and electroencephalogram, but also modeling technology for hidden
neural information transmission mechanisms such as the identification of characteristicfrequency patterns by applying decoding technology such as NMF and the estimation ofneuro activity coordination networks based on coherence analysis.
In order to address these issues, we would like to introduce the latest trends while showing
examples of analysis using actual data.
Keywords: Precision health, Biomedical informatics, Omics data, Neuro activity data

References[1] Aika Terada et al. Statistical significance of combinatorial regulations. Proc. Natl.
Acad. Sci., 110(32), 12996-13001, 2013.
[2] Knijnenburg et al. Logic models to predict continuous outputs based on binary inputs
with an application to personalized cancer therapy, Sci Rep 6;36812, 2016.

– 20–

CPS_1: Tuesday 13 (11:00-12:30) room RY102, page: 1

On two distance-based clustering methods for mixeddata
M. van de Veldena , A. Iodice D’Enzab and A. Markosc
a Erasmus Universiteit, Rotterdam, the Netherlands b Università degli studi di Napoli Frederico

II, Napoli, Italy c Democritus University of Thrace, Xanthi, Greece

Cluster analysis comprises of several unsupervised techniques aiming to identify a subgroup (cluster) structure underlying the observations of a data set. The desired cluster
allocation is such that it assigns similar observations to the same subgroup. Depending on the field of application and on domain-specific requirements, different approaches
exist that tackle the clustering problem. In distance-based clustering, a distance metricis used to determine the similarity between data objects. The distance metric can beused to cluster observations by considering the distances between objects directly or byconsidering distances between objects and cluster centroids (or some other cluster representative points) as is the case in K-means clustering. Most distance metrics, and hence
the distance-based clustering methods, work either with continuous-only or categoricalonly data. In applications, however, observations are often described by a combination ofboth continuous and categorical variables. Such data sets can be referred to as mixed or
mixed-type data. In this paper, we consider a method proposed by [1] that implements
K-means clustering using a specific distance measure based on co-occurrences of values,and compare it to so-called joint data reduction methods specifically designed for mixeddata.
Keywords: Cluster Analysis, Mixed Data, Dimension Reduction.

References[1] A. Ahmad, and L. Dey (2007). A k-mean clustering algorithm for mixed numericand
categorical data. Data and Knowledge Engineering, 63, 503–527.

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CPS_1: Tuesday 13 (11:00-12:30) room RY102, page: 1

Spatial CART Classification Trees
A. Bar-Hena , S. Geyb , J.-M. Poggic
a CNAM, Paris,

France, b Laboratoire MAP5, Univ. Paris Descartes, Paris, France, c LMO,
Univ. Paris-Sud Orsay & Univ. Paris Descartes, France

CART (Classification And Regression Trees) is a statistical method [1] designing tree
predictors for both regression and classification. We restrict our attention on the classification case with two populations. Each observation is characterized by some input
variables gathered in vector X and a binary label Y which is the response variable. The
general principle of CART is to recursively partition the input space using binary splits
and then to determine an optimal partition for prediction. The classical representationof the model relating Y to X is a tree representing the underlying process of construction of the model. If the explanatory variables are spatial coordinates, we get a spatial
decision tree and this induces a tessellation of the input space. A cell of this tessellation
corresponds to a leaf of the decision tree. For a leaf of this tree, the response variable Y
is constant and corresponds to the majority class of the observations belonging to this leaf.
The aim of this work is to adapt CART decision trees within the framework of binary
marked point processes by considering that the points inside the cells defined by the partition are a realization of a spatial point process, where the response variable Y can beseen as a mark of the point of position given by X. Following this idea, the variant of the
CART algorithm that we propose takes into account the spatial dimension of the marked
processes, and thus makes it possible to provide the user a partition of the plan defining
homogeneous areas of interaction between the two marks. While a classical assumption
for CART is to consider a sample of i.i.d. observations, our variant takes into accountspatial dependence through the quantification of the interaction between the two considered populations, i.e. the link between the labels of the points. Indeed, if CART is often
limited to the one most commonly used and implemented, presented in [1], there exist
several ways to build CART type decision trees, by changing the family of admissible
splits, the cost function or the stopping rule. We consider here the spatial dependence using the Ripley’s intertype K-function to suitably redefine the splitting criterion in CART.
Instead of being based only on the respective proportions of the marks, the splits will beselected according to the strength of interaction between the two populations.
The proposed procedure is implemented, illustrated on classical examples and compared
to direct competitors. SpatCART is finally applied to a tropical forest example.
Keywords: Classification Tree, Point process, Spatial data.

References[1] L. Breiman, J.H. Friedman, R.A. Olshen and C.J. Stone (1984). Classification andregression trees. Chapman & Hall.

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CPS_1: Tuesday 13 (11:00-12:30) room RY102, page: 1

Functional subspace clustering: a self-updating process approach
S.-Y. Shiua and T.-L. Chenba National Taipei University, b Academia Sinica

Many methods for functional data clustering have been proposed, including the use of adimension reduction technique prior to applying conventional clustering algorithms andmeasuring the shape similarities between data curves. In this talk we will present analgorithm that utilizes the idea of subspace clustering [1] for functional data. Such analgorithm usually provides a better clustering performance for functional data with acomplex structure.
We use self-updating process (SUP) [2] to identify cluster-specific subspaces, which arespanned by principal components from functional principal component analysis (FPCA).
The self-updating process continues updating the principal components of each data curve.
In the end of the process, data curves that have the same principal components are considered to be in the same cluster. Simulations as well as real datasets to show the performanceof this proposed algorithm will be presented.
Keywords: Functional data clustering, Functional principal component analysis, Selfupdating process.

References[1] L. Parsons, E. Haque and H. Liu (2004). Subspace clustering for high dimensional
data: a review. Acm Sigkdd Explorations Newsletter, 6(1), 90–105.
[2] S.-Y. Shiu and T.-L. Chen (2016). On the strengths of the self-updating processclustering algorithm. Journal of Statistical Computation and Simulation, 86(5):1010–
1031

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CPS_1: Tuesday 13 (11:00-12:30) room RY102, page: 1

A generalization of functional clustering for discrete
multivariate longitudinal data
Yaeji Lima and Hee-Seok Ohb
a Chung-Ang University, b Seoul National University

This paper presents a new model-based generalized functional clustering method fordiscrete longitudinal data, such as multivariate binomial and Poisson distributed data.
For this purpose, we propose a multivariate functional principal component analysis(MFPCA)-based clustering procedure for a latent multivariate Gaussian process insteadof the original functional data directly. The main contribution of this study is two-fold:modeling of discrete longitudinal data with the latent multivariate Gaussian process, anddeveloping of a clustering algorithm based on MFPCA coupled with the latent multivariate
Gaussian process. Numerical experiments including a real data analysis and a simulation
study demonstrate the promising empirical properties of the proposed approach.
Keywords: Functional clustering, Latent Gaussian process, Model-based clustering.

References[Chiou et al.(2014)] Chiou, J.-M., Chen, Y.-T. and Yang, Y.-F. Multivariate functional
principal component analysis: A normalization approach. Statistica Sinica 2014; 24:
1571–1596.
[Chiou and Li (2007)] Chiou, J.-M. and Li, P.-L. Functional clustering and identifying
substructures of longitudinal data. Journal of the Royal Statistical Society, Series B
2007; 69: 679–699.
[Hall et al.(2008)] Hall, P., Müller, H.-G. and Yao, F. Modelling sparse generalized longitudinal observations with latent Gaussian processes. Journal of the Royal StatisticalSociety, Series B 2008; 70: 703–723.
[Happ and Graven(2018)] Happ, C. and Greven, S. Multivariate functional principal component analysis for data observed on different (dimensional) domains. Journal of theAmerican Statistical Association 2018; 113: 649–659.

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CPS_2: Tuesday 13 (11:00-12:30) room RY105, page: 2

Random partition t-SNE
Szu-Han Lin, Ting-Li Chen and I-Ping Tu
Institute of Statistical Science, Academia Sinica

Data visualization has been recognized as an important tool in exploring the heterogeneity of high dimensional data. t-Distributed Stochastic Neighbor Embedding (tSNE)
proposed by Maaten and Hinton in 2008 has been the first to successfully separate the
10 digit groups of the MNIST data set into 10 clusters and has yielded a total of 7366
citations to date. This is a breakthrough in the data visualization field, but as the data
volume becomes huge, the computation becomes a burden and the application faces anoverwhelming barrier. Barnes-Hut tSNE (BH-tSNE) using tree-based algorithms was proposed in 2014 to accelerate tSNE. In this talk, we propose a randomized approach to speed
up and scale up tSNE. We first approximate the perplexity of tSNE by a randome sample method, and then iteratively apply tSNE on randomly partitioned groups. We showthat random partition tSNE (RP-tSNE) can significantly reduce computation complexity
and yet output a satisfying plot for the MNIST data set containing 70,000 digit images.
Furthermore, we provide a rigorous proof for the asymptotic consistency of RP-tSNE.
Keywords: Visualization, t-SNE, random partition.

References[1] L. V. D. Maaten and G. Hinton (2008). Visualizing data using t-SNE. The Journalof Machine Learning Research, 9(Nov), 2579-2605.
[2] L. V. D. Maaten (2014). Accelerating t-SNE using tree-based algorithms. The Journalof Machine Learning Research, 15(1), 3221-3245.

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CPS_2: Tuesday 13 (11:00-12:30) room RY105, page: 2

A visualization of aggregated symbolic data
J. Nakanoa , N. Shimizub and Y. Yamamotoc
a Chuo University, b The Institute of Statistical Mathematics, c Tokushima Bunri University

Recently we can easily get huge amount of data, mainly because of the rapid developmentof computer and sensor technologies. For such big data, it is difficult to focus on eachindividual data. Instead we are often interested in relationships among meaningful groups
of individual data. Then we express a group of individual data by several statistics andcall them “aggregated symbolic data” as a kind of symbolic data [1].
We consider the case where each individual data has both continuous and categorical variables. We know that mean and variance are simple descriptive statistics for a continuous
variable. For summarizing a categorical variable, we define two statistics to express alocation and a dispersion of a categorical variable. We also consider measures of association between two categorical variables, and between continuous and categorical variables,
together with the correlation between two continuous variables.
We summarize aggregated symbolic data by such statistics and propose an extended parallel coordinate plot to visualize them.
Keywords: Big data, Grouping, Parallel coordinate plot

References[1] L. Billard, and E. Diday(2006). Symbolic Data Analysis: Conceptual Statistics andData Mining, John Wiley & Sons, New York.

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CPS_2: Tuesday 13 (11:00-12:30) room RY105, page: 2

Statistical model based text mining and visualization:
application to longitudinally observed data
S. Izumia , T. Tondab , N. Kawanoc , and K. Satoha
a Shiga University, b Prefectural University of Hiroshima, c Hiroshima University

We propose a method to estimate and visualize effects of a covariate on the longitudinallyobserved text data([1]). Our method consists of series of analytical methods. First, we
extract keywords through a morphological analysis([2]). We let keyword’s appearance or
frequency at time point t as an outcome variable, assuming an outcome variable to beeither Bernoulli or Poisson distribution, respectively. Second, we estimate a semiparametric regression coefficient of a covariate for an outcome variable([3], ([4]). The regression
coefficient expresses time-varying effects of the corresponding covariate. Third, we classify summary vectors of estimated regression coefficient curve to group keywords([5]).
Finally, we visualize time-varying effects of a covariate in animated scatter plots via statistical software R. The procedure is demonstrated with Peace Declaration text data inJapan.
Keywords: Varying regression coefficient, Semiparametric model, K-means method.

References[1] S. Izumi, T. Tonda, N. Kawano, K. Satoh. (2017). Estimating and visualizing thetime-varying effects of a binary covariate on longitudinal big text data. The International Journal of Networked and Distributed Computing , 5(4), 243–53.
[2] S. Izumi, K. Satoh, and N. Kawano. (2015). Statistical classification and visualization based on varying coefficients model for longitudinal text data. Computational
Statistics, 28(1), 81-92(in Japanese).
[3] D. Ruppert, M. P. Wand, and R. J. Carroll. (2003). Semiparametric Regression.
Cambridge University Press, Cambridge.
[4] K. Satoh and T. Tonda. (2013). Statistical inference of semiparametric varying coefficients using mixed effects model. Japanese Journal of Applied Statistics, 42(1),1-10(in Japanese).
[5] T. Tonda, K. Satoh, and S. Izumi. (2018). About the difference of temporal changeof the view of peace in Hiroshima and Nagasaki peace declaration. The Journal of
the Hiroshima Medical Association, 71(4), 302–5(in Japanese).

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CPS_2: Tuesday 13 (11:00-12:30) room RY105, page: 2

Visualization of eﬀects on radiotherapy
M. Mizutaa
a Hokkaido University, Japan

Radiation therapy (radiotherapy) is one of the three or four major cancer treatment
methods. It attracts attention both from the viewpoint of quality of life and from theviewpoint of therapeutic eﬀect. The principal of radiotherapy is to kill tumor cells astargets while preserving normal tissues, especially organs at risk (OAR). Based on thisprinciple, Mizuta et al. [1] showed conditions under which radiotherapy, in particular
fractionated irradiation, is theoritically eﬀective. Sugano et al. [2] reported treatment
plans using the results. In radiation therapy, models of radiation eﬀects on tumors andOAR should be clarified. Takagi et al. [3] reported on a mathematical model for an
optimal irradiation scheme.
In this study, we will introduce the visualization of the relationship between the eﬀects ofradiation on tumor and OAR. In addition, we consider cases of handling multiple tumors
and multiple OARs. This helps to build an optimal treatment plan.
Keywords: LQ-model, Constrained optimization.

References[1] M. Mizuta, S. Takao, H. Date, N. Kishimoto, K. L. Sutherland, R. Onimaru,
and H. Shirato (2012) A Mathematical Study to Select Fractionation Regimen based on Physical Dose Distribution and the Linear-Quadratic Model. International Journal of Radiation Oncology, Biology, Physics, 84 (3), 829–833,
doi:10.1016/j.ijrobp.2012.01.004[2] Y. Sugano, M. Mizuta, S. Takao, H. Shirato, K. L. Sutherland and H. Date (2015).
Optimization of the fractionated irradiation scheme considering physical doses totumor and organ at risk based on dose volume histograms. Medical Physics, 42,6203–6210, doi: 10.1118/1.4931969[3] R. Takagi, Y. Komiya, K. L. Sutherland, H. Shirato, H. Date and M. Mizuta (2018).
Comparison of the average surviving fraction model with the integral biologicallyeﬀective dose model for an optimal irradiation scheme. Journal of Radiation Research,
59, i32–i39, doi: 10.1093/jrr/rrx084– 28–

CPS_3: Tuesday 13 (11:00-12:30) room RY106, page: 2

The Impact of Temperature on Nippon Professional
BaseballK. Kondaa and Y. Yamamotob
a Graduate school of Tokai University, b Tokai University

The regular season of the NPB (Nippon Professional Baseball) is from March to October.
In the regular season, there are rainy season and typhoon season. Each team performs
143 games in various places from Hokkaido to Okinawa in one season, so the weather
conditions of each game are very diﬀerent. In this reseach, we analyze how weather
conditions aﬀect individual stats.
The subjects of analysis in this reaserch are baseball data and meteorological data during
2016 to 2018. Baseball data is an oﬃcial record for each bat and can be acquired atNPB.jp. The meteorological data is estimated based on the observation data of thegeostationary earth environment observation satellite ”Himawari8” [1] [2]. The analysis
was conducted based on the previous research conducted using Major League (MLB)data [3]. We compare individual stats such as as runs scored, batting average, slugging
percentage, home run rate, and so on according to weather conditions and stadiums.
Finally, we propose a method to evaluate the performance with weather conditions, reﬂecting the analysis results.
Keywords: Baseball, Inpact of Temperature, Runs Expectancy.

References[1] K. Konda, Y. Yamamoto, H. Takenaka, H. Kimura, K. Fukuda, T. Nakajima (2018).
Development and application of data providing and visualization system for EMS. In
23rd International Conference on COMPUTATIONAL STATISTICS (COMPSTAT2018).
[2] H. Takenaka, T. Nakajima, A. Higurashi, A. Higuchi, T. Takamura and R. Pinker(2011). Estimation of solar radiation using a neural network based on radiative transfer. Journal of Geophysical Research, 116(D8), DOI: 10.1029/2009JD013337.
[3] B. Koch and A. Panorska (2013). The Impact of Temperature on Major League
Baseball. American Meteorological Society, 5, 359–366.

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CPS_3: Tuesday 13 (11:00-12:30) room RY106, page: 2

Assessing the eﬀects of time-dependent match environments on the soccer team performances
T. Obataa and S. Izumib
a Oita University, b Shiga University

It is well known that the number of goals by a team in a soccer match follows Poisson
distribution very well. Based on it, many models which assume that scores gained are
generated from a Poisson distribution with a certain intensity parameter have been discussed in order to assess the performances of soccer teams (e.g. [1, 2]). Izumi and Obata[3] have proposed a model for estimating the time-varying performances of teams belong
in a soccer league by introducing a varying coeﬃcient model proposed by Hastie andTibshirani [4]. They also have applied it to predict outcomes of upcoming matches [5].
Though their model didn’t include other than the time as explanatory variables, in thisresearch, we assess the eﬀects of additional covariates related match environments on theteam performances. Here we consider the place match held, the number of audiences
and weather. Eﬀects of covariates are assumed to be constant or functions of time. An
application to the data of 2018 season of J-League Division 2 which is a professional soccer
league in Japan is demonstrated.
Keywords: Soccer data, Poisson regression, Varying coeﬃcient.

References[1] S. J. Koopman and R. Lit, “A dynamic bivariate Poisson model for analysing andforecasting match results in the English Premier League,” Journal of the Royal Statistical Society: Series A, vol. 178, pp. 167–186, 2015.
[2] E. F. Saraiva, A. K. Suzuki, C. A. O. Filho, and F. Louzada, “Predicting footballscores via Poisson regression model: applications to the National Football League,”
Communications for Statistical Applications and Methods, vol. 23, pp. 297–319, 2016.
[3] S. Izumi and T. Obata, “Visualization of time transition of team performance insoccer league,” Communications of the Operations Research Society of Japan, vol.
63, pp. 628–634, 2018 (in Japanese).
[4] T. Hastie and R. Tibshirani, “Varying-coeﬃcient models,” Journal of the Royal Statistical Society: Series B, vol. 55, pp. 757–796, 1993.
[5] T. Obata and S. Izumi, “Predicting the outcome probability of the soccer match
based on the estimation of time-varying team performance,” Proceedings of the Asia
Paciﬁc Industrial Engineering and Management Systems Conference 2018, pp. 1–5,
2018.

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CPS_3: Tuesday 13 (11:00-12:30) room RY106, page: 2

Characterization of the formation structure in teamsports using Delaunay triangulation
Takuma Narizukaa and Yoshihiro Yamazakiba Chuo University, b Waseda University

In competitive team sports, such as football and basketball, to maintain formations is oneof the essential strategies. In this presentation, we introduce a general method to identify
formations in team sports [1].
Our method quantifies a formation of a team using the adjacency matrix A(t) of theDelaunay triangulation, whose components Aij (t) are given by{
1 if the Voronoi regions of players i and j are adjacent with each other at t,
Aij (t) =0 otherwise.
Owing to this quantification, change of the formation structure in a game is visualized by
the network (Delaunay network) as shown in Fig. 1. In addition, quantitative comparison
between formations is possible by introducing the dissimilarity between A(t) and A(t′ ) as
Dtt′ =N ∑N∑[Aij (t) − Aij (t′ )]2 .

i=1 j=1

We propose a clustering algorithm of formations based on hierarchical clustering anddemonstrate formation analysis using datasets comprising football and basketball games.
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Figure 1: A typical example of the Delaunay network of a football game (left panel) and
its adjacency matrix (right panel).
Keywords: Delaunay triangulation, Hierarchical clustering, Team sports. (Use at most
3 keywords)

References[1] Takuma Narizuka and Yoshihiro Yamazaki, Characterization of the formation structure in team sports, Proceedings of the Institute of Statistical Mathematics, Special
Topic，2017（in Japanese）.

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CPS_3: Tuesday 13 (11:00-12:30) room RY106, page: 2

Statistical approach toward automatic annotation ofhuman behavior
Wataru Takano
Center for Mathematical Modeling and Data Science, Osaka University

A real world is filled with continuous data, on which symbolic representation is established,
typified by language. The connectivity between continuous and symbolic data leads tocyber-physical system integrating a real world with a cyber space, or a methodology fora human-like artificial intelligence. Measurement techniques for human behaviors havebeen developed to record a wide variety of human motions with annotation texts attachedto them [1][2]. Mathematical modeling based on these human motions and annotations
is helpful to linguistically describe human behaviors.
Two aspects in language need to be taken into account to achieve the sentence generation from human behaviors; semantic and syntactic functions. We presents a statisticalframework that is decomposed into two modules; One for relations between behaviors andwords (semantic module), and one for word sequences (syntactic module). The semantic
module trains an optimal probability of a word being generated from a behavior, and thesyntactic module trains an optimal probability of transitioning among words such thatan annotation is the most likely to be generated from a behavior over training dataset.
Conversion from human behavior to its descriptive annotation is formulated as searching for the most probable word sequence given a human behavior. More specifically, we
compute a probability of each word from a human behavior according to the semantic
module, and subsequently compute a probability of rearranging the words into a word
sequence according to the syntactic module. A word sequence with the highest resulting
probability is selected for an annotation to describe the human behavior.
We tested annotation generation from human behavior on a dataset of human motions measured by an inertial measurement unit sensors, and attached annotations. The
motion data includes 84,868,680 frames, to which 62,207 annotations are attached. The
test qualitatively demonstrates that correct annotations are generated, and quantitatively
shows its validity with bilingual evaluation understudy score of 0.604 being achieved.
Keywords: Human Behavior, Annotation, Statiscal Model.

References[1] M. Plappert, C. Mandery, and T. Asfour. Learning a bidirectional mapping between
human whole-body motion and natural language using deep recurrent neural networks.
Robotics and Autonomous Systems, 109:13–26, 2018.
[2] W. Takano and Y. Nakamura. Statistical mutual conversion between whole body
motion primitives and linguistic sentences for human motions. International Journalof Robotics Research, 34(10):1314–1328, 2015.

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Short Oral: Tuesday 13 (13:40-14:20) room RY101, page: 3

A clustering-based approach for selecting core collection
Chen-An TsaiNational Taiwan University

Selecting a core collection from a large germplasm collection is an important strategy for
genetic improvement of crop breeding, especially facing the increasing threat of global
climate change. The concept of core collection of germplasm was first proposed by [1],as well as further development by [2] and [3]. The primary goal of the core collection is
to identify a core set of population containing as much genetic diversity as possible. It
has been interpreted and applied in many different ways to select a subset of inbreedinglines, populations, or individuals to present some whole resource of germplasm. Recently,
an enormous number of genetic makers are available from high-throughput genotyping
techniques. However, most methods and algorithms are less efficient and consistent for
selecting a diverse subset from such genome-wide data. Therefore, it is urgent to develop
a core collection method for use in the era of whole genome research.
In this study, we develop an algorithm for selecting a core set of lines from a large
germplasm collection that maximizes possible genetic diversity with a given user-defined
number of lines. A generalization of the methods for creating a core collection will be
described on a practical level, and illustrated with some examples. Furthermore, somepossibilities for wider applications of the concept will be described. Our proposed clusterbased algorithm is based on multiple correspondence analysis and multiple genetic diversity measures for core subset collection. We will compare the performance of our proposed
method with existing methods on selection of core subset collection in terms of diversity
measures through a variety of datasets. In addition, we will compare the practicability,
computation time, and accuracy on core subset collection. This core collection methodology can also be applied to make cross-sections of any set of germplasm accessions, orto create bulk populations with the intention of increasing the usefulness of the collections.
Keywords: Core collection, Cluster-based algorithm, Multiple correspondence analysis.

References[1] O. H. Frankel (1984) Genetic perspectives of germplasm conservation in Genetic
manipulation: impact on man and society. Cambridge: Cambridge University Press;
p.161-170.
[2] O. H. Frankel and A. H. D. Brown. (1984) Plant genetic resources today: a criticalappraisal in Crop Genetic Resources: Conservation & Evaluation. George Alien &Unwin Ltd., London; p.249-257.
[3] A. H. D. Brown (1989) Core collections: a practical approach to genetic resourcesmanagement. Genome, 31, 818-824.

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Short Oral: Tuesday 13 (13:40-14:20) room RY101, page: 3

Mixtures of probabilistic principal component analysis
for NMAR missing data
Y. Moriokaa , K. Taniokab and H. Yadohisaa
a Doshisha University
b Wakayama Medical University

Probabilistic principal component analysis (PPCA) [1] is a multivariate statistical procedure that assumes continuous latent variables. It is widely used for data compression,visualization, exploratory data analysis, and pattern recognition. We can obtain the parameters of PPCA using an expectation-maximization (EM) algorithm. The advantage
of an EM algorithm is that it can handle data with missing values; PPCA can therefore
be applied to such data. However, if the missing value cannot be ignored, that is, if themissing data mechanism is not missing at random (NMAR) [4], the true data structure
may not be captured due to resulting bias caused by the missing values. To solve thisproblem, PPCA for NMAR missing data has been proposed [3]. On the other hand,
PPCA has been extended to a mixture model to capture the latent cluster structure indata [2], which is known as mixtures of PPCA. The parameters of this method can also be
obtained using an EM algorithm. However, when NMAR missing values exist in the data,in which the latent cluster structure is assumed to exist, mixtures of PPCA cannot beapplied in this situation. Therefore, we propose new mixtures of PPCA that can handle
such values. The parameters of our proposed method are obtained using an EM algorithm similar to [3]. We demonstrate the validity of the proposed method by conducting
a simulation study and empirical data analysis.
Keywords: EM algorithm, Not missing at random, PPCA.

References[1] M. Tipping and C. Bishop (1999). Probabilistic principal component analysis. Journalof the Royal Statistical Society: Series B, 61(3), 611–622.
[2] M. Tipping and C. Bishop (1999). Mixtures of probabilistic principal component
analysers. Neural Computation, 11(2), 443–482.
[3] M. Geraci and A. Farcomeni (2016). Probabilistic principal component analysis to
identify profiles of physical activity behaviours in the presence of non-ignorable missing data. Journal of the Royal Statistical Society: Series C, 65(1), 51–75.
[4] R. Little and D. Rubin (2002). Statistical analysis with missing data (2nd edition),
Wiley.

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Short Oral: Tuesday 13 (13:40-14:20) room RY101, page: 3

Fuzzy clusterwise generalized structured component
analysis for mixed data
I. Takasawaa , K. Taniokab and H. Yadohisaa
a Doshisha University, b Wakayama Medical University

Generalized structured component analysis (GSCA)[1] was proposed as a componentbased approach to structural equation modeling (SEM) - a widely used methodology
which clarifies the interdependency between observed variables and latent factors underlying the data. Researchers prefer GSCA because it does not assume that observed
variables follow a normal distribution and is applicable even for a small sample size. By
iterating initialization, global minimum parameters can be calculated to estimate GSCA
parameters for which the alternating least squares (ALS) method is used. Several variations of GSCA have been proposed. For example, in the fuzzy clusterwise generalized
structured component analysis (FCGSCA) method[3], fuzzy clustering and GSCA is simultaneously carried out as the data is assumed to be heterogeneous. In addition, GSCA
for three-way data has also been proposed[4]. Although both qualitative and quantitative
data are often obtained in the field of psychology and sociology, GSCA can be applied
only to quantitative data. Moreover, non-linear GSCA[2] which uses optimal scaling (OS)to quantify categorical variables is suggested to handle qualitative data. However, where
both quantitative and qualitative data exist, FCGSCA can not be used. We propose,
therefore, fuzzy clusterwise generalized structured component analysis (FCGSCA) formixed variables data.
Keywords: Clustering, Generalize structured component analysis, Mixed variables.

References[1] H. Heungsun and Y. Takane (2004). Generalized structured component analysis.
Pscchometrika, 69(1), 81–99.
[2] H. Heungsun and Y. Takane (2009). Nonlinear generalized structured component
analysis Behaviormetrika, 37(1), 1–14.
[3] H. Heungsun, W. S. Desarbo and Y. Takane (2007). Fuzzy clusterwise generalized
structured component analysis Psychometrika, 72(2), 181.
[4] J. Y. Choi, S. Yang, A. Tenenhaus, and Y. Takane (2017). The Annual Meeting of
the Psychometric Soiety. Springer, Charm.

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Short Oral: Tuesday 13 (13:40-14:20) room RY101, page: 3

Uniqueness-dissociated factor analysis modified for providing sparse loadings and extended common factors
Masamichi Ito & Kohei Adachi
Osaka University

Factor analysis (FA) aims to extract the common factors (CF) and unique factors (UF)underlying observed variables. Here, CF serve for explaining the variations of all variables.
On the other hand, each of UF, which has the one-to-one correspondence to each variable,
is considered ideal, if it explains exclusively the variation of the corresponding variable [1].
The ideal exclusiveness of UF is guaranteed in the uniqueness-dissociated FA (UD-FA)
presented recently by [2], in which UF is completely dissociated from the remaining part.
This part, which can also be extracted by minimum rank FA [3], is referred to as extended
CF (ECF), as it includes CF as a reduced version.
In this paper, we propose a sparse variant of UDFA (Spaud-FA) and a procedure for
identifying ECF. In the former Spaud-FA, the matrix of CF loadings is constrained to
be sparse, i.e., include a specified number of zeros, which facilitates interpreting CF.
For Spaud-FA, we present an iterative algorithm. It leaves the task of identifying theloadings of ECF, since they have rotational indeterminacy. Thus, we also propose acolumn complement Procrustes (CcProc) procedure for rotating the ECF loadings so as
to match the CF counterparts. Here, it is required to compliment columns in the CF
loading matrix, since its columns are less than the ECF ones. An iterative algorithm ispresented for CcProc. The Spaud-FA algorithm is assessed with a simulation study, andthe Spaud-FA procedures with CcProc are illustrated with real data examples.
Keywords: Minimum rank factor analysis, Cardinality constraint, Procrustes rotation

References[1] Adachi, K. (2019). Factor analysis: Latent variable, matrix decomposition, and constrained uniqueness formulations. WIREs Computational Statistics, HYPERLINK
https:// doi.org/10.1002/wics.1458.
[2] Stegeman A.(2016). A new method for simultaneous estimation of the factor model
parameters, factor scores, and unique parts. Computational Statistics and Data Analysis, 99, 189-203[3] ten Berge J.M.F, Kiers H.A.L (1991). A numerical approach to the approximate andthe exact minimum rank of a covariance matrix. Psychometrika, 56, 309-315– 36–

Short Oral: Tuesday 13 (13:40-14:20) room RY101, page: 3

LARS-type estimation procedure for generalized linear models
Y. Hirose
Hokkaido University

We introduce a sparse estimation method for generalized linear models. The proposed
method is based on Least Angle Regression (LARS) [2] and is described in terms ofRiemannian geometry. The basic idea is to run the LARS algorithm in the tangent spaceof a manifold of a statistical model. A few extensions of LARS have been proposed
which are based on Riemannian geometry (information geometry/diﬀerential geometry):for example, [3] and [1]. The existing methods take advantage of the dual structure of themodel manifold, which require computational costs. Our method utilizes a part of thedual structure and uses the original LARS algorithm as a part. Thus, the method allows
us to compute the estimators easily. We compare our method with some regularization
methods in numerical experiments.
Keywords: Information geometry, Sparse estimation

References[1] L. Augugliaro, A. Mineo, and E. Wit (2013). Diﬀerential geometric least angle regression: a diﬀerential geometric approach to sparse generalized linear models. Journalof the Royal Statistical Society Series B, 75, 471–498.
[2] B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani (2004). Least angle regression(with discussion). The Annals of Statistics, 32, 407–499.
[3] Y. Hirose and F. Komaki (2010). An extension of least angle regression based on theinformation geometry of dually flat spaces. Journal of Computational and Graphical
Statistics, 19, 1007–1023.

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Short Oral: Tuesday 13 (13:40-14:20) room RY101, page: 3

A geometric estimation method for autoregressive models
D. G. Gonçalves, Y. Hirose
Hokkaido University

We propose a method for parameter estimation and model selection for autoregressivemodels on the context of time series. Being an application of the bisector regression
algorithm [1] our method maintains that algorithm’s efficiency and variable selecting nature. Our algorithm is iterative and is efficient once the number of iterations it takes, thenumber of estimates it outputs and the number of parameters of the desired model are
all equal. This algorithm selects parameters by reducing one parameter on the output
estimated model at the end of each iteration. Our method utilizes the autocovariance andinverse autocovariance values of the estimated models as parameters on the algorithm.
We run numerical experiments with our algorithm and compare its efficiency on variableselection with the graphical lasso algorithm [2], a sparse and shrinkage estimator thatthrough penalized-likelihood methods performs model selection and estimation. We run
the numerical experiments on different orders of autoregressive models and different sizesof data samples.
Keywords: Autoregressive Model, Information Geometry, Sparse Estimation

References[1] Y. Hirose and F. Komaki (2010). An extension of least angle regression based on
information theory of dually flat spaces. Journal of Computational and Graphical
Statistics, 19(4), 1007–1023.
[2] M. Yuan and Y. Lin (2007). Model selection and estimation in the Gaussian graphicalmodel. Biometrika, 94(1), 19–35.

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CPS_4: Tuesday 13 (13:40-14:20) room RY102, page: 3

Clustering Partially Masked Images from Wafer Bin
Maps
S.-L. Jeng
Department of Statistics, National Cheng Kung University, Taiwan

In this work, we establish a procedure for the clustering of partially masked images fromwafer bin maps (WBMs). A WBM is an image of bin codes from many circuit probe
tests on a wafer after a semiconductor manufacturing process. Certain special bin code
patterns of WBMs provide engineers information to identify the potential manufacturing problems ([1],[2] and [4]). However, the WBMs may be masked by a large area orseveral small areas due to the order effect of measurements for bin codes. One of the
major challenges comes from the problem that many WBMs have various proportions ofnoise. To solve these problems, we develop a method for image reconstruction of thosemasked WBMs. We use a modified Radon transformation to extract features from thereconstructed WBMs in order to provide a better clustering result ([3]). We explore ourprocedure by varying the levels of noise and the areas of masking through a simulation
study. The image patterns of WBMs in the simulation are observed from a specific kindof wafer product.
Keywords: Clustering, Image Reconstruction, Radon Transformation.

References[1] M. H. Hansen, V. N. Nair, and D. J. Friedman (1997). Monitoring wafer map datafrom integrated circuit fabrication processes for spatially clustered defects. Technometrics, 39(3), 241–253.
[2] J. Kim, Y. Lee, and H. Kim (2018). Detection and clustering of mixed-type defectpatterns in wafer bin maps. IISE Transactions, 50(2), 99–111.
[3] S. Tabbone, L. Wendling, and J.-P. Salmon (2006). A new shape descriptor definedon the Radon transform. Computer Vision and Image Understanding, 102(1), 42–51.
[4] M.-J. Wu, J.-S. Jang, and J.-L. Chen (2015). Wafer map failure pattern recognitionand similarity ranking for large-scale data sets. Semiconductor Manufacturing, IEEE
Transactions on., 28(1), 1–12.

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CPS_4: Tuesday 13 (13:40-14:20) room RY102, page: 3

Analyzing Model Bias in Cryo-EM
Single-Particle Image Processing
S.-H. Wanga , Y.-C. Yaoa , W.-H. Changb , and I.-P. Tuaa Institute of Statistical Sciences, Academia Sinica, Taipei 115, Taiwan,b Institute of Chemistry, Academia Sinica, Taipei 115, Taiwan

Single particle cryogenic electron microscope (cryo-EM) has become a popular method
to determine the structure of biological molecules to near-atomic resolution. When cryoEM is applied to imaging biological molecules, the data is recorded in a micrographcontaining many particle projections in unknown orientations. A big challenge of cryo-EM
image analysis is that the signal to noise ratio is extremely low (SNR < 0.1), because themolecules are photographed with low-exposure to minimize structural degradation causedby radiation. 2D clustering, a crucial step in cryo-EM analysis, is to group projections ofsimilar particle orientations such that the resulting averages can greatly enhance the signal
to noise ratio of those abundant views and allow them to be labeled. Meaningful clustering
depends on good image alignment, for which all possible rotations and translations are
exhaustively searched to find the most fitted solution. However, image alignment for
highly noisy data can be strongly biased toward the reference model, which is referred
as model bias phenomenon. In this talk, I will show how we investigate model bias froma mathematical and statistical perspective. We propose an index to quantify model bias
and provide the consistency and asymptotic theorems. This is a joint work with Yi-Ching
Yao, Wei-Hau Chang and I-Ping Tu.
Keywords: Cross-correlation, cryo-EM, extreme-value distribution, high dimensional,
model bias

References[1] Cai, T., Fan, J., and Jiang, T.-F. (2013). Distributions of angles in random packing
on spheres. Journal of Machine Learning Research 14, 1837–1864.
[2] Cai, T. and Jiang, T.-F. (2012). Phase transitoin in limiting distriubtions of coherenceof high- dimensional rankdom matrices. Journal of Multivariate Analysis, 207, 24-39.
[3] Frank, J. (1975). Averaging of low exposure electron micrographs of non-periodic
objects. Ultramicroscopy, 1, 159-162.
[4] Frank, J. and Al-Ali, L. (1975). Signal-to-noise ratio of electron micrographs obtainedby cross correlation. Nature, 256, 376-379.
[5] Henderson, R. (2013). Avoiding the pitfalls of single particle cryo-electron microscopy: Einstein from noise. Proc. Natl. Acad. Sci. USA, 110, 18037-18041.

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CPS_5: Tuesday 13 (13:40-14:20) room RY105, page: 4

Joint oblique rotation in multiple correspondence analysis and its properties
N. Makinoa
a Osaka University

Multiple correspondence analysis (MCA) is one of the widely used method for analyzing
nominal variables and assigning the vectors of scores to categories and objects. As isalready known, an MCA solution has rotational freedom and can be rotated so that thesolution is interpretable. Rotation is classified into orthogonal and oblique. Orthogonalrotation can be viewed as the special case of oblique rotation and the use of obliquerotation is usually preferable in practice.
However, an obliquely rotated MCA solution has a negative aspect; joint graphical representation of the categories and objects cannot be justified after oblique rotation due tothe violation of homogeneity. Let F be n-objects × p-dimension component score matrix,
G be n-objects × K-categories indicator matrix of observed data, W be K-categories ×
p-dimension category score matrix, and S be p-dimension × p-dimension rotation matrix.
Then, the loss of homogeneity in MCA is expressed as ∥F − GW∥2 , which is the loss
function of homogeneity analysis [1]. In the obliquely rotated solution, ∥FS′ −1 − GWS∥2
does not equal to ∥F − GW∥2 except for an orthogonal rotation case. It indicates thatoblique rotation in MCA should only be used for dimensional interpretation and cannot
be applied for graphical representation [2] .
In this study, we report that the loss function of homogeneity analysis can be rewritten as
minimization of distance between subspaces spanned by quantified object and componentscore, and be rotated obliquely without aﬀecting the objective function value. We also
prove that proposed formulation can be regarded as the extension of joint orthogonalrotation technique in MCA [3] to oblique one. Real data example is given in our presentation.
Keywords: Multiple correspondence analysis, Rotation

References[1] A. Gifi (1990). Nonlinear multivariate analysis. Wiley, Chichester.
[2] K. Adachi (2004). Oblique promax rotation applied to the solutions in multiple correspondence analysis. Behaviormetrika, 31(1), 1–12.
[3] K. Adachi and T. Murakami (2011). Nonmetric multivariate analysis. From principal
component analysis to multiple correspondence analysis. Asakura shoten, Tokyo (inJapanese).

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CPS_5: Tuesday 13 (13:40-14:20) room RY105, page: 4Some applications in multivariate methods with alternating least squares
Y. Moria , H. Katayamaa , T. Yoshiokaa , M. Kurodaa and M. Iizukaba Okayama University of Science, b Okayama University

In many situations of data analysis, categorical (nominal and ordinal) and numerical data
are collected together. Since alternative least squares (ALS) is useful for such situations
because ALS with optimal scaling quantifies all categorical variables in the date to homogeneous numerical ones, so that we can deal with all variables uniformly as numericalones. This means that we easily apply ordinary multivariate methods developed for numerical data to any collected data including categorical variables. Therefore, using ALS,
some new approaches, extensions and further computational eﬃciencies can be considered
for mixed measurement level data.
Here three applications of multivariate methods in the context of principal component
analysis (PCA) using ALS with optimal scaling are proposed. First one is a variableselection in nonlinear PCA (NLPCA): A variable selection procedure proposed by [1] is
modified by implementing ALS with optimal scaling in the selection so as to uniformly
deal with any levels of variables. This new variable selection then makes us select a reasonable subset of variables from mixed measurement level variables, and numerical examples
illustrate how the selection is performed in a variety of cost saving selections. Second
one is a new algorithm for reduced k-means clustering (RKM): RKM is extended so as
to deal with categorical variables which are quantified by replacing the dimensionality
reduction part by NLPCA. A numerical experiment on categorical data, which is artificially generated from numerical data, demonstrates that the RKM with NLPCA provides
more similar cluster configuration to original one of numerical data than one based on
non-quantified categorical data. Third one is a further application of ALS: since ALS
algorithm is accelerated under a certain condition, any methods with ALS-type iteration
under the condition such as NLPCA can be accelerated ([2]). Numerical experiments
for NLPCA demonstrate that the acceleration improves the performance of computation
about 3 times of ordinary ALS for the number of iterations and CPU time.
Keywords: Categorical analysis, alternating least squares, computational eficiency

References[1] Mori, Y., Iizuka, M., Tanaka, Y., Tarumi, T. (2006): Variable Selection in Principal
Component Analysis, In: Härdle, W., Mori, Y., Vieu, P. (eds), Statistical Methods
for Biostatistics and Related Fields, 265-283, Springer[2] Mori. Y., Kuroda, M., Makino, N. (2017). Nonlinear Principal Component Analysis
and Its Applications. JSS Research Series in Statistics, Springer.

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CPS_6: Tuesday 13 (13:40-14:20) room RY106, page: 4

Trigonometric forms of Fourier series estimator in nonparametric regression for longitudinal data
M. F. F. Mardiantoa,b,∗, S. H. Kartiko c and H. Utami ca Ph.D. Student, Department of Mathematics, Gadjah Mada University, Yogyakarta, Indonesia.
b Department of Mathematics, Airlangga University, Surabaya, Indonesia.
c Department of Mathematics, Gadjah Mada University, Yogyakarta, Indonesia.
∗

Corresponding Author : m.fariz.fadillah.m @ fst.unair.ac.id

In regression analysis there are three approaches. Nonparametric regression is one approach in regression analysis. Nonparametric regression is one solution when the data haslack goodness of fit, and the linear regression cannot fulfill assumption tests. Nonparametric regression has good flexibility to estimate data pattern based on the suitable
curve smoothly. One of the estimator that often be used in nonparametric regression is
Fourier series. The advantage using nonparametric regression estimator based on Fourier
series estimator, this estimator able to overcome the data with oscillation pattern, in thiscase can be approximated by trigonometric function. Fourier series estimator is suitable fortrend seasonal data pattern [1]. Recently, the nonparametric regression is not only useful incross section data but also in longitudinal data. Longitudinal data can find out the changes
which occur in subjects, because the observations are repeated for each sub-ject.
Longitudinal data consist of time series and cross section data. However, the study about
Fourier series estimator in longitudinal data is a new study. This study proposes all of
possibility from trigonometric basis that often be used in Fourier series. In this study, thediscussion is about nonparametric regression estimator based on Fourier series globally, andFourier sines series. Both of estimator developed by using Fourier cosines series estimator
concepts that have been proposed by Bilodeau in 1992. The result from this study are three
Fourier series estimators that have corresponding form in nonpara-metric regression for
longitudinal data. The estimator formula result can be applied in meteorology case inIndonesia. A prediction of rainfall in five big cities in Java island have been done based on
temperature, wind speed, humidity, and time. The best estima-tor for prediction can be
determined based on the smallest Generalized Cross Validation (GCV) and Mean Square
Error (MSE) for an oscillation parameter that be determined. It gives an impact to result
the biggest determination coefficient for selected estimator. The best result for prediction is
Fourier series globally that include cosines and sines basis.
Keywords: Fourier series estimator, nonparametric regression, longitudinal data.

References[1] M. F. F. Mardianto, S. H. Kartiko, and H. Utami (2019). Forecasting trend-seasonal
data using nonparametric regression with kernel and Fourier series approach. In Proceedings of the Third iCMS2017 Springer Series, Langkawi, 343–349.

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CPS_6: Tuesday 13 (13:40-14:20) room RY106, page: 4Forecasting the term structure of yields bond
D.Rosadia , Muslimb and A.Sihabuddinca Department of Mathematics, Gadjah Mada University
b Department of Mathematics, Universitas Jambic Department of Computer Science, Gadjah Mada University

In this paper, we discuss the problem of modeling and forecasting the daily yield curve ofgovernment bond. We consider various models in the Nelson-Siegel (NS) class of the yield
curve models as discussed and proposed in Muslim, Rosadi, Gunardi and Abdurakhman(2019). As in Muslim et.al (2019), we estimate the model using using a genetic algorithm
method and an SQP estimation procedure. Then, we forecast various parameters of allmodels using automatic ARIMA, automatic ANN and automatic hybrid Wavelet-ARIMANeural Networks methods (see e.g. Rosadi, 2019) and compare their performances withthe forecasted results using Vector Autoregression (VAR) model (see also alternative approaches in e.g. Rosadi, Nugraha and Dewi, 2011). The forecasted parameters are thenused to calculate the forecasted daily yield curve of the government bonds. We provide
empirical studies using Indonesian Government Bond data, obtained from Indonesia financial market. We also discuss the possibility of applying these methods to forecast theyields curve of corporate bonds and sukuk.
Keywords: Yield Curve, Nelson-Siegel models, Forecasts.

References[1] Muslim, D.Rosadi, Gunardi and Abdurakhman (2019). Calibrating the nelson-siegel
model classes and their estimation using hybrid-genetic algorithm approach: Casestudy of indonesian government bonds. Journal of Theoretical and Applied Information Technology, 97(2), 409–422[2] D.Rosadi (2019). Automatic algorithms for univariate time series forecasting using SARIMA and hybrid Wavelet-ARIMA-Neural Networks Model. Presented in
ECOSTA 2019, NCHU Taiwan, June 25-27, 2019.
[3] D.Rosadi, Y.A.Nugraha and R.K.Dewi (2011). Forecasting the Indonesian Government Securities Yield Curve using Neural Networks and Vector Autoregressive Model.
Proc. 58th World Statistical Congress, Dublin (Session CPS071).

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Application of Bayesian Spatiotemporal Varying Coefficients to the Mortality from Ischemic Stroke
Kuo-Jung Lee and Sheng-Mao Chang
Department of Statistics, National Cheng-Kung University, Taiwan

A Bayesian spatiotemporal generalized linear regression with varying-coefficient model is
proposed to examine geographic variation of the medical prescription use for ischemic
stroke and to have a clearer understanding of association of the relevant risk factors suchas comorbidities, medication and environmental society with mortality rate in ischemic
stroke. The spatial heterogeneity of coefficients for important factors that may affect themortality in ischemic stroke across 349 townships in Taiwan. By applying spatial-temporalmodels, we can understand the spatial variation in risk to ischemic stroke. It then turns
out that, based on the findings, we can properly arrange medical resources and reduce thelife-threatening damage caused by the uneven distribution of medical resources. The data
was collected from a retrospective cohort study using 2004-2012 National health insurance
research database.
Keywords: Bayesian, Spatiotemporal, Varying Coefficient Model.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Bayesian Estimation of Impression Discriminant Ability in Paired Comparison
S. Hashimoto, T. Shimozono and N. Nagata
Kwansei Gakuin University

The paired comparison method is eﬃciently used to evaluate the impressions of multiple
samples, when ranking or absolute evaluation of such samples is diﬃcult. Thus, thismethod is widely used the design, sensory evaluation, and marketing fields. In a paired
comparison of the these fields, participants are assumed to have suﬃcient impression
discriminant ability; however, it is unclear whether the assumptions are satisfied. The
discriminant abilities of a participant are considered to depend on various external factors
or attributes such as gender and age. To obtain the relation between discriminant ability
and such external factors, discriminant abilities should be obtained numerically.
In this study, to address this problem, we estimate the ability of each participant using
Bayesian modeling. The model is an extension of Thurston’s Case 5[1], and diﬀerences inability between participants are illustrated as diﬀerences in the randomness of the evaluation. In paticular, herein, when comparing two stimuli relative to subjective impression,a person with a low score is assumed to evaluate more randomly than that with a highscore.
Valid results, in which diﬀerences between two groups are illustrated as diﬀerences ofparameters, were obtained in an analysis of participants with and without experience of
training of playing music evaluating the impressions of chords.
Keywords: Paired Comparison,Dscriminant Ability, Bayesian Modeling.

References[1] Thurstone, L. L. (1927) A law of comparative judgments. Psychological Review, 34,
273-286.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Statistical analysis for livestock sectors in Indonesia
based on factors that related to the development ofindustrial revolution
A. Pradanaa, Z. N. Amalia a, I. Hanifah a and M. F. F. Mardiantob,∗a Students, Department of Mathematics, Airlangga University, Surabaya, Indonesia.
b Department of Mathematics, Airlangga University, Surabaya, Indonesia.
∗

Corresponding Author : m.fariz.fadillah.m @ fst.unair.ac.id

In this industrial revolution era, the development of technology is rapidly growing. The
development of technology, led to many new innovations in several fields of activity. One ofthem is the livestock sub-sector. The potential which can be developed by the livestock subsector is very efficient in the utilization of natural resources have been available and provide
jobs [1]. In the livestock sub-sector has a major contribution to the economic growth inIndonesia. Indonesia Livestock Agency stated that livestock sub-sector con-tributed
15.87% to Gross Domestic Product (GDP) in 2017. Meanwhile, based on the data which isreleased by Central Bureau of Statistics in August 2017, total national workforce in the
livestock sub-sector accounted for 3.17% and livestock sub-sector also contributes to absorb
11.51% of the workforce in the agricultural sector. From these data, it can be interpreted
that the livestock sub-sector has potential to increase the economy in Indonesia and provide
jobs for Indonesia citizens. Every region in Indonesia has potential to develop the livestock
sub-sector. So, the clustering with k mean is used to divide three clusters. Based on themethod of cluster analysis, meat and egg are in cluster 1. In clus-ter 2, people on average insome provinces do not consume pork, horse meat, eggs quail and buffalo meat. In cluster 3,
common people on average in some provinces prefer the consumption of all types of eggs. In
addition, we also analyzed the influence of indicators that related to industrial revolution
development and human development index. The literacy rate and the number of mobile
phone users are regressed to livestock production in Indonesia. By using nonparametric
regression analysis with truncated spline estima-tor, the result become the indicatorvariable of the industrial revolution proficiency level that significantly affect to livestock
production in Indonesia. In this study, researchers suggest the stakeholder in order toincrease the motivation of people, especially the mil-lennial in order to help contribute to
the development of livestock sub-sector in Indonesia.
Keywords: K-Means cluster, truncated spline nonparametric regression, Indonesia livestock sector.

References[1] P. K. Thornton (2010). Livestock production: recent trends, future prospects. Philosophical Transactions Journal, 365(1554), 2853–2867.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

K-means clustering on text data
V. N. Lestaria , and Subanarb
a b Universitas Gadjah Mada

Individuals and organizations tend to use electronic media to store textual informationand documents. It takes time for readers to retrieve relevant information from a collection of unstructured documents, it is easier to find documents when classified by group.
Document grouping that is generally in the form text data can be done by clustering
method, one of the algorithms that can be used the k-Means algorithm. To simplify theclustering process, first step is preprocessing documents. Preprocessing documents consists of folding cases, tokenization, filtering and stemming. Tf-Idf is used to weight theterms obtained in preprocessing documents. After the document passes the preprocessing
stage, the documents can be grouped using methods from k-means clustering.
Keywords: K-Means Clustering, Text Mining, Preprocessing.

References[1] J. Han, and M. Kamber (2006). Data Mining Concept and Techniques Second Edition.
Morgan Kaufman Publishers, Burlington.
[2] P. Jaiswal (2007). Clustering Blog Information (Master’s Thesis Projects). San Jose
State University.
[3] P. Lama (2013). Clustering System Based On Text Mining Using The K-Means Algorithm (Bachelor’s Thesis). Turku University of Applied Sciences.
[4] S. T. Deokar (2013). Text Document using K-Means Algorithm. International Journalof Technology and Engineering Science, 1(4), 282–286.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Chinese Word Segmentation by Considering Dependence between Adjacent Words (DSSV 2019)
K.-H. Wua and H.-M. Hsueha
a Department of Statistics, National ChengChi University, Taiwan

Recently, there is a great achievements of digital humanities due to advance of information technology. Very often, a digital humanities study involves text data and researchers
become more interested in text data analysis. Chinese language, distinct from otheralphabet-based language such as English, is character-based. Further, because there isno space between characters in a sentence, the very first and important data processing
step is word segmentation in texts. Deng et al. (2016) [1] proposed a unsupervised word
segmentation method, named ”TopWORDS”, for Chinese texts on the basis of a ”worddictionary model (WDM)”. The authors regard the unavailable segmentation information
as missing data and apply the E-M algorithm to estimate the probability distribution of
occurrence of a word. The dynamic programming method is employed to increase thecomputational efficiency during the estimating procedure. The TopWORDS method is
unsupervised in the sense that users do not need to provide a dictionary beforehand.
Moreover, the authors show that the method produces moderate performance in several
real examples, including two classic Chinese literatures and modern texts collected on
Internet. However, TopWORDS considers a strong assumption that words in phrases orsentences in texts are mutually independent. This method hence ignores the syntactic patterns and seems less adequate. In this study, we modify the TopWORDS by considering
word-to-word dependence. In specific, correlations between adjacent words are assumed.
The modified method is called SeqWORDS. Introducing the dependence among words
elevates the complexity of the SeqWORDS procedure, particularly in the likelihood of
the E-M algorithm and the iterative formula applying dynamic programming. The performance of SeqWORDS are investigated through several Chinese classical literatures.
Overall, we found that SeqWORDS are comparable with existing methods.
Keywords: Chinese word segmentation, dictionary, dynamic programming, E-M algorithm, word-to-word dependence.

References[1] K. Deng, P. K. Bol, K. J. Li, and J. S. Liu (2016). On the unsupervised analysis of
domain-specific Chinese texts. Proceedings of the National Academy of Sciences of
the United States of America, 113(22), 6154–6159.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Growing Bigger and More Accurate with GSBPM (partfive)
Very Basukia and Nurani Apriliaba Government Statistical Training Center, b STIS 57 Social Division

Big data mining provides volatile price of imported goods and services within five working
days at month end. Generic Statistical Bussiness Process Model (GSBPM) is facilitating the sharing of software components, and providing a framework for process quality
assessment and improvement. (Exploring the relationship between DDI, SDMX and the Generic
Statistical Business Process Model Steven Vale, United Nations Economic Commission for Europe).

Figure 1: sub process 1·5 2·5 3·5 4·5 5·5 6·5 7·5 gsbpm version five.
1. Big data mining depends on open access world wide web. From several hundred
imported goods and services of interest only between five to nine imported goods andservices can be captured in real time by big data mining. End user may be alerted forany specific imported goods and services which may or may not be of interest.
2. One option is to disseminate well accepted representative of particular quality ofgoods or service similar to burgernomics (https://www.economist.com/news/2018/07/11/the-bigmac-index) or iPad mini (https://www.cfr.org/blog/new-geo-graphics-ipad-mini-index-should-calmtalk-currency-wars). Standardized univariate and bivariate [x-µ]/σ are not recommended.
Derived variate such as [x1 -µ1 ]*[x2 -µ2 ] can be introduced. Both µ1 and µ2 are taken fromprevious data repository such as non - volatile memory.
3. Significance of statistical data [cut off value] is traditionally derived from statistical
moments such as sample covariance and univariate standardized distance. Since big data
may not be statistically representative sample last minute big data may not be significancein statistical sense. Disseminated statistical data is restricted only to provide bivariate
profile represented by statistical moments.
4. Current effort is turning to web service availability. Big data mining is formerly usedby consolidation of production account sub-directorate using Yahoo Application Program
Interface. Less keyword almost certainly translates to less ambiguity and in turn fasterbig data mining result. Local language manual search may differ from US English language manual search.
5. Big data mining offers various variates which may or may not adhere to statisticalpractice. For statistical purpose it is plausible to narrowly define specific variate. A wellknown example of real time big data mining is currency exchange rate by the hour. In
September 2018 local currency goes down against leading international currency such as
dollar.
6. Aggregate data includes temporal index and regional index (not necessarily spatial
index). Result of big data mining may not be decisively identified as short term seasonal
effect or long term trend shift. For highly fluctuated volatile price big data high jump or
low plunge may be regarded as confirmatory to final data prior to dissemination.
7. Staff members answer public data user question. Imported goods and services pricefluctuation is affected by currency exchange rate which may not be visible in survey andin aggregate figure such as µ1 or µ2 or other µn . If departure from aggregate figure isdeclared as statistically significant then the fluctuation is prepared as note.
Keywords: data mining, statistical index– 50–

Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Establishment Approach Survey to Update Household
Approach Survey (part one) [HES]Isni Sukrianaha , Aulia Dinib and Rahmiati Rahimc
a STIS 55 Social Division, b STIS 53 Economic Division, c STIS 46 Computer Division

Background
By recent ministry of interior regulation Indonesia has thirty four provinces. Eachprovince is administratively divided into regions, there are a little more than half a thousand regions. In simple terms urban region is called big city given numerical code startingwith 71. Currently there is a province without any big city that is a province having only
districts. Currently there are three provinces having a district declared as provincial capital. Most provinces declare a big city as provincial capital.
Provinces having a district declared as provincial capital means non-urban capital. Within
an urban big city there exist at least one rural market and similarly within any rural region such as district an urban market may exist. Therefore an urban marke may be foundin least developed region.
Recommendation
Possible update of Household Expenditure Survey [HES] by establishment approach survey for certain goods and services. A recommendation is an additional entry in [HES]questionnaire that is name of shop where household buys goods & services as mathematical relationship. Manufactured goods & services in rural market facilitates establishment
approach data collection by means of Universal Product Code [UPC] and similar code
as well as internet-based transaction for services including electricity & cellular communication. A recommendation is to include additional small number of urban households
in [HES] and to be updated by establishment approach survey of urban market. Annual
trend of urban household expenditure can be compared with that of other survey such as
establishment approach Consumer Price Survey [CPS].
Replication
For the first time a household approach survey tentatively named Internet Purchasing
Survey [IPS] is carried out covering initial two thousand respondents. This very narrow
scope hopes to be easily and correctly interpreted by respondents within small town aswell as rural region such as district. Only a handful of local on-line stores are covered thatis foreign-based store without physical local address is excluded. More specifically only
prepaid goods purchase is covered that is Cash on Delivery is excluded. Services such ascc
GoSend⃝delivery is excluded. Services such as Uber⃝transport is excluded.
Contribution
Tentatively named Internet Purchasing Survey [IPS] may be able to relate purchased
goods with respective local on-line stores (sellers) including their corresponding home
page (Universal Resource Locator). [IPS] will be expensive since it will require household
visit by regional statistical oﬃce staﬀ member in the evening and in week end. Once thelist of prepaid purchased household goods are obtained updating of price can be hopefully
carried out by establishment approach survey with exact URL. Update can hopefully be
executed by regional statistical oﬃce staﬀ member using oﬃce equipment in the oﬃcehour.
Keywords: Establishment, Household, Mathematical relationship

References[1] undang-undang (law) number 20 year 2012.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Pricing of agricultural insurance premium in Indonesia with the option approach
Y. W. Saria and Gunardib
a ,b Universitas Gadjah Mada

Agricultural insurance provides protection, guarantees, or compensation to farmers as theowner of an insurance policy who suffers losses due to crop failure, where one of the losses
is due to extreme and uncertain weather. In Indonesia, most farmers are still reluctant toparticipate in agricultural insurance programs because the amount of premiums, collateral for crop failures obtained, and claim requirements are not worth the loss that theyexperience. Therefore, in this study we will discuss how to determine agricultural insurance premiums using the barrier option approach, where the risk of loss is seen from theweather factor. This research is expected to become one of the references in improving
the sustainable agricultural insurance system in Indonesia.
Keywords: Barrier option, agricultural insurance premium, weather index.

References[1] A. N. Okine (2014). Pricing of Index Insurance Using Black-Scholes Framework : A
Case Study of Ghana. Illinois State University.
[2] D. J. Higham (2004). An Introduction to Financial Option Valuation, Mathematics,
Stochastics, and Computation. Department of Mathematics University of Strathclyde, Cambridge.
[3] D. Y. Hwang and F. S. Shie (2015). The Barrier Option in Deposit Insurance with
Bankruptcy Cost. Research Gate Publication.
[4] F. Black and M. Scholes (1973). The Pricing of Options and Corporate Liabilities.
Journal of Economic Theory, 10, 239–257.
[5] I. A. G. K. Putri, K. Dharmawan, and N. K. T. Tastrawati (2017). Perhitungan
Harga Premi Asuransi Pertanian yang Berbasis Indeks Curah Hujan Menggunakan
Metode Black Scholes. E-Jurnal Matematika, 6(2), 161–167.
[6] P. Wilmott (2001). Paul Wilmott Introduces Quantitative Finance. John Willey andSons Ltd, Chichester.
[7] W. Estiningtyas (2015). Asuransi Pertanian Berbasis Indeks Iklim : Opsi Pemberdayaan dan Perlindungan Petani Terhadap Risiko Iklim. Jurnal Sumberdaya Lahan,9(1), 51–64.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Comparison between estimation methods for undirected
networks
T. Yamasakia
a Graduate School of Comprehensive Human Sciences, University of Tsukuba

Research on the network approach to psychopathology have been conducted since around2008, and the approach has received considerable attention in recent years [1]. In thisstudy, we compared estimation accuracies of four estimation methods for undirected networks: an empirical partial correlation based approach, adaptive lasso, graphical lasso,
and birth-death MCMC. Assuming that the number of variables are much fewer thanthat of sample size, which is common in psychological researches, we conducted a seriesof simulations.
The results suggested that among the four approaches, the method, which is based on a
directly obtained partial correlation matrix, is most useful. However, in some conditions,estimation accuracies of the methods using adaptive and graphical lasso were higher thanthose of the others. Therefore, according to situations, it is necessary to use these methods properly.
Keywords: Undirected network, Partial correlation, Psychology

References[1] E. I. Fried, C. D. van Borkulo, A. O. J. Cramer, L. Boschloo, R. A. Schoevers, and
D. Borsboom (2017). Mental disorders as networks of problems: a review of recent
insights. Social Psychiatry and Psychiatric Epidemiology, 52, 1–10.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Hazard ratio function based on kernel estimator inmodelling stunting toddler’s height
S. H. Kartikoa∗ and M. F. F. Mardianto b,ca Department of Mathematics, Gadjah Mada University, Yogyakarta, Indonesia.
b Ph.D. Student, Department of Mathematics, Gadjah Mada University, Yogyakarta, Indonesia.
c Department of Mathematics, Airlangga University, Surabaya, Indonesia.
∗ Corresponding Author : s kartiko @ yahoo.com

ABSTRACT
Hazard ratio is a measure of an effect from the intervention on an outcome in certain
times. Hazard ratio is related to survival analysis. In this case, hazard ratio estimation isdetermined based on nonparametric approach. Nonparametric approach is more flexible
when estimate unknown form of data pattern and the result forms suitable smooth curve.
One of the function that is often used in this nonparametric approach is kernel function.
Kernel function is a certain ratio function, so that it is suitable to be used as an estimatorof hazard ratio function. Moriyama and Maesono in 2018 stated that the Asymptotic
Mean Squared Error (AMSE) of Kernel estimator of the hazard ratio function was smaller
than it is of the usual hazard estimator and is often used in survival analysis theoretically[1]. In this paper, hazard ratio function based on kernel estimator is applied by considering
its Mean Square Error (MSE) and Generalized Cross Validation (GCV) to get the optimal
bandwidth which is a smoothing measure in kernel estimator.
Hazard ratio function estimator that based on kernel estimator is then applied to stunting toddler’s height data in Indonesia. Stunting can increase the risk of morbidity andmortality for toddler. The stunting toddler data is collected from Public Health Officein Malang Region which is the second largest region in East Java, the most populous
province in Indonesia after West Java. The number of stunting toddler in this region
is very high, based on Public Health Office data, that is in 2018, 20% toddlers in thisregion was stunting. The important things is to give solution about stunting problem bydetermining failure risk for stunting toddler in this area. Hazard ratio gives interpretation
about the rate of failure risk for observed period. The result of the application is, thestunting toddlers have good survival rate that can be proved by the small failure risk
using the hazard ratio function output.
The result gives the small AMSE, MSE, and GCV for the selected bandwidth which is
logically reasonable, because the government effort has already given nutrition intensively
for stunting toddler in order to decrease the number of stunting toddler in Indonesia.
Keywords: Kernel estimator, hazard ratio function, stunting toddler.

References[1] T. Moriyama, and Y. Maesono (2018). New kernel estimators of the hazard ratio
and their asymptotic properties. In Annals of the Institute of Statistical Mathematics,
Tokyo, 1–25.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Two forms of complete Fourier series estimator to predict contribution for ”Suroboyo” bus payment system
R. A. Ariyantoa, R. Andriawan a, D. A. Husada a and M. F. F. Mardiantob,∗a Students, Department of Mathematics, Airlangga University, Surabaya, Indonesia.
b Department of Mathematics, Airlangga University, Surabaya, Indonesia.
∗ Corresponding Author : m.fariz.fadillah.m @ fst.unair.ac.id

Nonparametric regression is a regression approach that is used for uncertain data pattern
between predictor and response variables, such as linear, quadratic, and cubic. The flexibility from nonparametric regression is very good because this approach can estimate thedata pattern based on the suitable regression curve. One of the estimators that used be in
nonparametric regression is Fourier series. Fourier series estimator has good performancefor modeling trend seasonal data pattern [1]. Based on the literature, in the same Fourier
series type, there are two different forms of Fourier series. In this case, comparison basedon two forms Fourier series used to a real application. The Fourier series with sines andcosines bases are used to this case. The first estimator is a nonparametric regression with
Fourier series equation which involves π component in the trigonometric functions. The
second estimator is a nonparametric regression with generalization of Fourier series equation without involving π component in the trigonometric functions. Either the first or
the second estimators are applied to predict the percentage of contribution for Suroboyo
bus payment system for observation periods. Suroboyo Bus is public transportation thatuses plastic waste as payment. Suroboyo Bus is one of the solutions from Surabaya’s
government to reduce plastic waste in Surabaya. The result from this research is twomodels of a percentage of Suroboyo Bus contribution to handling plastic waste and compare both models to determine the best model. The selected model for every form ofcomplete Fourier series can be determined based on the smallest Generalized Cross Validation (GCV) and Mean Square Error (MSE) for each oscillation parameters that bedetermined and considering parsimony model. The best model that be selected for prediction is Fourier series with the smallest GCV, and MSE, with considering the parsimonymodel. It gives an impact to result in the biggest determination coefficient for selected
estimator, and the smallest Mean Absolute Percentage Error (MAPE) in prediction.
Keywords: Fourier series estimator, nonparametric regression, payment system withmineral water packaging.

References[1] M. F. F. Mardianto, S. H. Kartiko, and H. Utami (2019). Forecasting trend-seasonal
data using nonparametric regression with kernel and Fourier series approach. In Proceedings of the Third iCMS2017 Springer Series, Langkawi, 343–349.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Investigating the dependence of two different species
from presence-absence maps
Y. M. Changa , W. H. Wua and C. H. Huangaa Department of Statistics, Tamkang University, Danshui, New Taipei, Taiwan

This research is intended to investigate the dependence of two different species frompresence-absence maps. The chi-square test is used to test if the distribution of these two
species are independent. If the hypothesis of independence is rejected, we further computethe p-values to see if there is any significant positive dependence or negative dependence
between species. Under the assumption of binomial distribution or Poisson distribution,
we propose six different methods to compute the p-values. The test performance of the
proposed methods is compared with previous methods through simulation. An application is demonstrated.
Keywords: co-occurrence, binomial, presence-absence map.

References[1] J. A. Veech (2013). A probabilistic model for analysing species co-occurrence. Global
Ecology and Biogeography, 22, 252–260.
[2] J. A. Veech (2014). The pairwise approach to analysing species co-occurrence. Journalof Biogeography, 41, 1029–1035.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Interval Estimation for Nonconformance Proportionof Air Quality Index
T. Y. Lina , C. T. Liaob and Y. Y. Liaoaa Department of Applied Mathematics, Feng Chia University, Taichung, Taiwan,b Division of Biometry, Institute of Agronomy, National Taiwan University, Taipei, Taiwan

Air pollution is one of the most important global environmental issues. Taiwan Environmental Protection Agency (EPA) is currently using an Air Quality Index (AQI) to measureits national air quality. The objective of this study is to assess the air quality per hour
every month in Taichung City of Taiwan from 2014 to 2016 based on the nonconformanceprobability of the AQI index proposed by Taiwan EPA. The nonconformance probability
is defined as the probability that a characteristic of interest falls outside of an acceptanceregion. A lower confidence bound for the nonconformance probability is applied to test
whether a AQI index value per hour every month exceeds a warning threshold. So thegovernment could issue warnings using the statistical inference from the historical data
analysis. An unbalanced two-way random effects model is presented for fitting the AQI
index values. We propose three estimation methods, including a t-based, an adjusted tbased and a generalized pivotal quantity (GPQ) based methods, to construct the required
lower confidence bound. According to a detailed simulation study, a hybrid method of
the t-based and the adjusted t-based estimators is recommended for practical use.
Keywords: Conformance proportion, Generalized confidence interval, Variance component

References[1] H. I Lee, and C. T. Liao (2014). Unilateral conformance proportions in balanced andunbalanced normal random effects models. Journal of Agricultural, Biological, andEnvironmental Statistics, 19, 202–218.
[2] S. Werrahandi (1993). Generalized confidence intervals. Journal of the American
Statistical Association, 88, 899–905S..

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Unbiased estimation equation under f -separable extension of squared and Itakura-Saito distances
Masahiro Kobayashia and Kazuho Watanabeaa Department of Computer Science and Engineering, Toyohashi University of Technology

We focus on unbiased estimation equations in a class of M-estimation problems. In recent
years, robust parameter estimation methods have been proposed based on divergencessuch as density power divergence [1] and γ-divergence [2]. In these methods, unbiased
estimation equations are assumed to ensure the consistency of estimators. However, toobtain an unbiased estimation equation, it is necessary to add the integral of the powerof the statistical model as the bias correction term. If this term is exactly zero, it isdesirable for the following two reasons: the estimator can be written in a simple form
and the estimation equation can be regarded as normalized and hence the estimator ispotential for minimizing the latent bias even if the proportion of outliers is large [3].
In [4], we considered the M-estimation of θ with the f -separable distortion measures∑ni=1 f (dϕ (xi , θ)) for the data sample {x1 , · · · , xn }, where f is an increasing function anddϕ is the Bregman divergence associated with the convex function ϕ. If f is concave,the estimator is shown to be robust. We investigate the conditions for the estimation
equation associated with this distortion measure to be unbiased. Focusing on the squared
and Itakura-Saito distances as examples of Bregman divergences for location and scalefamilies, we characterize the conditions under which the bias correction term vanishes.
We show that for a particular class of f , the estimation equation is unbiased withoutbias correction, if the data-generating distribution is symmetric in the case of the squared
distance, and if it is a Gamma distribution with known shape parameter in the case of
the Itakura-Saito distance. Furthermore, we discuss the consistency of the estimatorsand their latent bias. Asymptotic relative eﬃciencies are derived and compared with theM-estimation based on density power divergence and γ-divergence.
Keywords: f -Separable distortion measures, Itakura-Saito distance, M-estimators.

References[1] A. Basu, I. R. Harris, N. L. Hjort, and M. C. Jones (1998). Robust and eﬃcientestimation by minimising a density power divergence, Biometrika, 85(3), 549–559,[2] H. Fujisawa and S. Eguchi (2008). Robust parameter estimation with a small bias
against heavy contamination. Journal of Multivariate Analysis, 99(9), 2053–2081.
[3] H. Fujisawa (2013). Normalized estimating equation for robust parameter estimation.
Electronic Journal of Statistics, 7, 1587–1606.
[4] M. Kobayashi and K. Watanabe (2019). Generalized Dirichlet-process-means for fseparable distortion measures. arXiv:1901.11331.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

A robust RUV-testing procedure via γ-divergence
Hung Hung
Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taiwan

Identification of differentially expressed genes (DE genes) is commonly conducted in modern biomedical research. However, unwanted variation inevitably arises during the datacollection process, which can make the detection results heavily biased. Various methods
have been suggested for removing the unwanted variation while keeping the biologicalvariation to ensure a reliable analysis result. Removing Unwanted Variation (RUV) has
recently been proposed for this purpose, which works by virtue of negative control genes.
On the other hand, outliers frequently appear in modern high-throughput genetic data,
which can heavily affect the performances of RUV and its downstream analysis. In thiswork, we propose a robust RUV-testing procedure (a robust RUV procedure to remove
unwanted variance, followed by a robust testing procedure to identify DE genes) via
γ-divergence. The advantages of our method are twofold: (i) it does not involve any
modeling for the outlier distribution, which makes it applicable to various situations; (ii)
it is easy to implement in the sense that its robustness is controlled by a single tuning
parameter γ of γ-divergence, and a data-driven criterion is developed to select γ. Whenapplied to real data sets, our method can successfully removed unwanted variation, andwas able to identify more DE genes than conventional methods.
Keywords: γ-divergence; RUV; Unwanted variation.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Generalized interventional approach for causal mediation analysis with causally ordered multiple mediators
Sheng-Hsuan Lin
Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan

Causal mediation analysis has shown the advantage of mechanism investigation. Underconditions with causally ordered mediators, path-specific effect (PSE) is introduced forspecifying the effect mediated by a certain combination of mediators. However, mostof PSEs are unidentifiable. To address this issue, an alternative approach, called interventional analogue of PSE (iPSE), is widely applied to effect decomposition. Previous
literature for multiple mediators mainly focused on discussing the case of two mediators
due to the complexity of the mediation formula. A generalized method under the settings
with the arbitrary number of mediators is attractive to study the causal parameter identification as well as statistical estimation. This paper propose a generalized interventional
approach to discussing the effect mediated by ordered multiple mediators. It provides a
general definition of iPSE by a recursive formula, assumptions for non-parametric identification, and a regression-based method and a G-computation algorithm to estimate all
iPSEs. This approach is applied to a Taiwanese cohort study for exploring the mechanism among hepatitis C virus on mortality through hepatitis B virus, liver function, andhepatocellular carcinoma.
Keywords: Causal mediation analysis, Path-specific effect, Multiple mediators– 60–

Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Confidence bands for quantile residual life function
with clustered survival data
Yunchan Chia and Rui Ting Wangaa Department of Statistics, National Cheng Kung University, Tainan, Taiwan

Clustered survival data arise naturally from biomedicine, econometrics and sociology studies. It is useful to estimate quantile residual life for medical devices, such as ventilatingtubes in ears, artificial joints, and dental implant. For example, the patients with joint
replacement may want to know how long the artificial joints will still be functioning given
the fact that the artificial joints are used for at least t years.
For clustered right-censored data, to the best of our knowledge, there are no available
confidence bands for quantile residual life of a single group. Moreover, failure to accountfor intra-cluster correlation can lead to bias inference. Therefore, this paper will construct the confidence band for quantile residual life functions of a single group based ona normalized quantile residual life process [1], which does not involve unknown density
function. Because the theoretical quantile of the distribution of the sup-norm of the limiting process cannot be obtained analytically, the bootstrap method is applied to obtain
the empirical quantile of the distribution of the sup-norm of the limiting process.
A simulation study will be conducted to investigate the accuracy of the proposed confidence bands. The otology study conducted by [2] is used to demonstrate the proposed
methodologies.
Keywords: quantile residual life function, confidence band, clustered survival data

References[1] T. H. Tsai (2015). Confidence bands for quantile residual life function with left trucated and right censored data. Ph.D. Dissertation, Department of Statistics, National
Cheng Kung University, Tainan, Taiwan[2] C. T. Le, and B. R. Lindgren (1996). Duration of ventilating tube: a test for comparing two clustered smaples of censored data. Biometrics, 328-334.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Mapping of The PM2.5 Sources Effect to Respiration
and Cardiac Health Based on Geographically Weighted
Regression in Indonesia
R. Andriawana, D. A. Husadaa, I. Hanifah a and M. F. F. Mardiantob,∗a Students, Department of Mathematics, Airlangga University, Surabaya, Indonesia.
b Department of Mathematics, Airlangga University, Surabaya, Indonesia.
∗

Corresponding Author : m.fariz.fadillah.m @ fst.unair.ac.id

One of the effects of technological advancements is the increasing amounts of air pollutants. World Health Organization estimated that 91% of world population inhales
unhealthy air and there are 4.2 million deaths caused by exposure to air pollutants eachyear. One of the most dangerous air pollutants is PM2.5, particulate matter less than 2.5
microns that small enough to infiltrate human bodies into lungs and bloods circulation.
Several sources of PM2.5 includes vehicle’s emission, cigarette’s smoke and combustion.
However, Indonesia’s government decides to measure their Air Quality Index (AQI) with
PM10 as indicators instead of PM2.5. Thus, Indonesia’s government is unable to monitor theamount of PM2.5 in the country which might lead to negative impact on inhabitant’s
respiration and cardiac health. Therefore, statistical modeling can be used as an alter-native
to measure sources of PM2.5 effect to inhabitant health in Indonesia. Because of Indonesia’svast and varied geography, Geographically Weighted Regression (GWR) is considered an
appropriate statistical modeling. GWR is a locally linear regression model which makes
local parameter estimates for each location of observation. Parameter for each location
could be estimated by Weighted Least Square (WLS) with different spatial weighting
including gaussian distance function, exponential function, bi square function and tricube
kernel function. The best spatial weight for the model is decided by the high-est R2 with thesmallest Mean Square Error (MSE) and Akaike’s Information Criterion (AIC). The result is
GWR model with the best spatial weight based on comparison of R2, MSE, and AIC for
mapping the PM2.5 sources effect to respiration and cardiac health in Indonesia.
Keywords: Geographically Weighted Regression, PM2.5, air quality index.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Modelling Of Corn Productivity In Central Java, Indonesia Using Hyper-Local Geographically Weighted
Regression
Herni Utami
Department of Mathematics, Gadjah Mada University, Indonesia

Weather changes can affect corn production in an area. The amount of corn production in
an area depends on the area of agricultural land in the area. The wider the area of agriculture will result in more corn production. Different geographical and climate conditions ineach district are expected to have a spatial effect on the productivity of corn in a district.
Therefore, in this study we used the productivity data of corn (quintal per hectare) andsome weather attributes in all districts in Central Java Province. The present study will
discuss corn productivity modeling based on weather attributes in all districts in Central
Java using the Hyper-Local Geographically Weighted Regression (GWR) model. The
model simultaneously optimizes both local model selection and local kernel bandwidth
specication [1].
Keywords: Hyper-local GWR, Corn Productivity, Bandwidh.

References[1] A. Comber, Y. Wang, Y. Lu, X. Zang, and P. Harris (2018), Hyper-local geographically weighted regression: extending GWR through local model selection and local
bandwidth optimization. Journal of Spatial Information Science, 17(2018), 63–84.
[2] R. Cai, D. Yu, and M. Oppenheimer (2012), Estimating the Effects of Weather Variations on Corn Yields using Geographically Weighted Panel Regression, presented at
The Agricultural and Applied Economics Associatuins 2012 AAEA Annual Meeting,Seatle Washington.
[3] W. Schlenker and M.J. Roberts (2008), Estimating the Impact of Climate Change
on Crop Yields: The Importance of Nonlinear Temperature Effects, NBER Working
Paper No. 13799.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Local linear regression methods for interval-valued data
Jungteak Janga and Kee-Hoon Kangaa Department of Statistics, Hankuk University of Foreign Studies, Yongin, Korea

The interval-valued data, which is one of the symbolic data, is given as an interval inwhich the observation object is not a single value. Regression analysis of interval-valued
data began to be studied in the early 2000s. References include [2] for linear regression,[3] for Nadaraya-Watson estimator using the kernel function and [1] for a nonlinear regression method. In this paper, we review a nonparametric regression model using the kernel
function and a nonlinear regression model for the interval-valued data. We also propose
applying the local linear regression model, which is one of the nonparametric methods, tothe interval-valued data. Using each of the methods presented in this paper, simulations
based on the several distributions of the center point and the range are conducted. Considering various conditions, it is confirmed that the performance of proposed approachshows better performance than the others.
Keywords: Cross-validation, Nonlinear regression, Nonparametric regression.
Acknowledgement: This work was supported by Basic Science Research Program
through the National Research Foundation of Korea funded by the Ministry of Education(NRF-2017R1D1A1B03035026).

References[1] E. A. Lima Neto and F. A. T. De Carvalho (2017). Nonlinear regression applied tointerval-valued data. Pattern Analysis and Applications, 20, 809–824.
[2] L. Billard and E. Diday (2000). Regression Analysis for Interval-Valued Data. in H.
A. L. Kiers, J.-P Rassoon, P. J. F. Groenen, and M. Schader(eds.), Data Analysis,
Classification, and Related Methods. Springer-Verlag, Berlin, 369–374.
[3] R. A. A. Fagundes, R. M. C. R. De Souza, and F. J. A. Cysneiros (2014). Interval
Kernel Regression. Interval Kernel Regression, 128, 371–388.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Structural equation modeling for society satisfaction
analysis of the e-toll system in Indonesia
R. W. Ulyaa, A. S. Syamsudin a and M. F. F. Mardiantob,∗a Students, Department of Mathematics, Airlangga University, Surabaya, Indonesia.
b Department of Mathematics, Airlangga University, Surabaya, Indonesia.
∗ Corresponding Author : m.fariz.fadillah.m @ fst.unair.ac.id

Structural Equation Modeling (SEM) is an effective model testing and improving methodthat enables with theoretical models to be tested as a whole, and SEM can explain the cause
and effect relationship of the variables in mixed hypotheses which are related to the models
based on statistical dependence [1]. SEM can be viewed as a combination of factor analysis
and regression or path analysis. In this paper, SEM was applied to find out the effect of etoll system service quality on society satisfaction. This research is important to do so could
be seen whether the e-toll has use values. Data were obtained by distributing questionnaires
online according to procedures which were then analyzed. The questionnaires have beentested based on validity and reliability. The dimensions of service quality are assessed fromthe elements of reliability, responsiveness, assurance, empathy and physical evidence.
Based on the results of the study, society was satisfied with the use of the e-toll system on
the toll road. However, there are two of five dimen-sions of service quality that are verysignificant, namely the dimensions of reliability and assurance. Recommendations that canbe given for the Indonesia government and the related parts are providing top-up outlets ateach toll gate and rest area, guaranteeing the suitability of the transaction costs with bills,
and make all transaction activities along the toll road use a simple payment instrumentwith e-toll.
Keywords: Structural Equation Modeling, society satisfaction analysis, e-toll system.

References[1] R. Larry (2015). Structural Equation Modeling (SEM): Concepts, Applications andMisconceptions. Nova Publisher, New York.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Structural equation modeling in motivation analysis
for millennial participation related to general electionsin Indonesia
Z. N. Amaliaa, R. W. Ulyaa, D. R. Hastuti a and M. F. F. Mardiantob,∗a Students, Department of Mathematics, Airlangga University, Surabaya, Indonesia.
b Department of Mathematics, Airlangga University, Surabaya, Indonesia.
∗ Corresponding Author : m.fariz.fadillah.m @ fst.unair.ac.id

Structural Equation Modeling (SEM) is a statistical technique used to build and test thestatistical models are usually in the form of causal models [1]. SEM is a combination fromfactor analysis, path analysis and regression. This method is a statistical approach thatserves to test hypotheses about the relationship between observed variables and latentvariables. In this paper, SEM is applied to determine the motivation of the millennial
generation for the general election 2019 in Indonesia. This research is very important toknow the millennial generation’s motivation to have awareness to use their voting rights or
not abstentions. Data was obtained by distributing questionnaires online according toprocedures which were then analyzed using SEM. Millennial’s motivation is seen from theknowledge of the millennial generation on voting rights commitments in the 2019 gen-eral
election in Indonesia. The dimensions of the 2019 general election in Indonesia were judged
by views on election contestants and knowledge on 2019 general election. Based on the
result of the study, millennial generation is committed to using voting rights in the 2019
general election. All indicator variables from knowledge of the 2019 general elec-tion are
significant to the millennial generation’s commitment to use their voting rights.
Researchers suggest that the Election Committee in Indonesia can improve the activity
system a series of democratic parties that were carried out before, namely equalization ofanti-abstention and introduction of the contestant’s vision and mission and warning toabstentions.
Keywords: Structural Equation Modeling, millennial participation, Indonesia generalelection.

References[1] R. Larry (2015). Structural Equation Modeling (SEM): Concepts, Applications andMisconceptions. Nova Publisher, New York.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Variable selection in propensity score using Adaptive
Bridge for confounding adjustment
Shuji Ando
Tokyo University of Science

This clinical study aimed to obtain an unbiased estimate average treatment eﬀect. Similar
to observational studies, when treatments are not randomized, it is necessary to adjust
confounders to estimate average treatment eﬀects without bias. Propensity score methods[1] are often used to adjust for confounders. Since the propensity score is unknown, it
should be estimated using a statistical model. All confounders need to be included in thepropensity score model to estimate an unbiased average treatment eﬀect. Previously, allcovariates considered potential confounders were included in the propensity score model.
However, recent clinical studies yield numerous covariates. Furthermore, recent studies[2] have reported that the variance during the estimation of average treatment eﬀects
is decreased upon including confounders and outcome predictors in the propensity scoremodel, and their bias and variance are increased by including covariates not related tothe outcomes. Thus, the variable selection method is necessary to accurately identify
confounders and outcome predictors. Outcome-Adaptive Lasso [3] has been proposed asa method to select covariates associated with the outcome when constructing a propensity
score model. Although this method can select covariates associated with the outcome,the rate of selecting covariates unrelated to the outcome is not low. This study, proposesa variable selection method of propensity score using Adaptive Bridge [4] to reduce theselection rate of covariates unrelated to the outcome and its utility has been verified withnumerical examples.
Keywords: Adaptive Lasso, Average treatment eﬀect, Model selection, Observational
studies.

References[1] Rosenbaum, P. and Rubin, D. (1983). The central role of the propensity score in
observational studies for causal eﬀects. Biometrika 70, 41–55.
[2] Brookhart, M., Schneeweiss, S., Rothman, K., Glynn, R., Avorn, J. and Sturmer,
T. (2006). Variable selection for propensity score models. American Journal of Epidemiology 163, 1149–1156.
[3] Shortreed, S. M. and Ertefaie, A. (2017). Outcome-Adaptive Lasso: Variable selection
for causal inference. Biometrics 73, 1111–1122.
[4] Chen, Z., Zhu, Y. and Zhu, C. (2016). Adaptive bridge estimation for highdimensional regression models. Journal of Inequalities and Applications 2016,
2016:258.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

New sparse modeling of sample Mahalanobis distanceY. Kobayashi
Faculty of Science and Engineering, Teikyo University, Utsunomiya, Japan

Sparse modeling, such as Least Absolute Selection and Shrinkage Operator (LASSO) forregression [1] has gained interest in variable selection to extract the essential data variables
and prevent over-learning problems. Therefore, sparse modeling has also been applied to
study the anomaly distance (AD). Thus far, only a sample covariance matrix S of learning
samples x has been made sparse, for example, by applying graphical LASSO [2]. However,the AD, such as the sample Mahalanobis distance (MD), of test sample y was not madesparse. Hence, this study was focused on making the AD of test sample y sparse.
In principle, ordinal sample MD D2 is given by D2 = (y − x̄)′ S −1 (y − x̄) = z ′ z, where x̄ isthe mean of the learning samples, and z is the studentized score vector (SSV) of y, i.e., z
is the solution of linear equation y − x̄ = S 1/2 z. I propose a new kind of sparse MD, D̂2 ,given by D̂2 = ẑ ′ ẑ, where ẑ is the sparse solution of the equation obtained by applying thecoordinate-decent method to solve LASSO. This sparse MD cancels the unstable eﬀect ofnumerical error on the sample MD as follows.
When learning samples x follow the p-variate normal distribution with population eigenvalues λ such that one λ0 = 0 and the other λ > 0 at the Monte Carlo simulation, sample
eigenvalue l0 of S corresponding to λ0 becomes slightly positive under the influence of thenumerical error, and D2 becomes unstable owing to l0 . Subsequently, distributions of theelement corresponding to l0 of the SSV of test sample y were simulated as a(y), b(y), √andc(y) for the ordinal,√ridge, and sparse MDs, respectively. Here, a(y) = (y − x̄) · v0 / l0 ,b(y) = (y − x̄) · v0 / l0 + ρ, and c(y) = ẑ(0) , where both x and y follow the same normal
distribution with dimensionality p = 7, v0 is the sample eigenvector corresponding to l0 ,regularizing constant ρ = 10−30 ∼= l0 , and ẑ(0) is the element corresponding to l0 of ẑ.
The result shows that the sparse MD is useful for numerical computing. a(y) has a broad
distribution because of a numerical error, and b(y) has a narrower distribution aroundy = 0 than a(y). However, c(y) degenerates at y = 0 correctly as λ0 = 0, i.e., the eﬀectof the numerical error is removed. However, for each of the other elements of the SSV ofy, all the three distances show the same distribution.
Keywords: Anomaly distance, LASSO, numerical error.
Acknowledgment. This is supported by JSPS KAKENHI Grant number JP19K20224.

References[1] T. Hastie, R. Tibshirani, and M. Wainwright (2015). Statistical Learning with Spacity,
the Lasso and Generalizations. CRC Press, Boca Raton.
[2] T. Idé, A. C. Lozano, N. Abe, and Y. Liu (2009). Proximity-based Anomaly Detection
using Sparse Structure Learning. In Proc. 2009 SIAM International Conference on
Data Mining, 97–108.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Spatial Model For The Prevalence of Leprosy In South
Sulawesi, Indonesia
R. M. Sabila and R. Sastriba Statistics Indonesia, b Polytechnics of Statistics STIS-Indonesia

The prevalence of leprosy is the number of leprosy cases per 10.000 people. Based on datafrom the Ministry of Health, the highest prevalence of leprosy was in South Sulawesi. The
number of leprosy cases in an area may be influenced by the number of leprosy case in
the neighborhood area due to the movement of the air. So that, the location of area need
to be included in analysis of leprosy. The aim of this study is to identify the variables
that spatially aﬀect the prevalence of leprosy in South Sulawesi and modelling it. This
study uses data from the Ministry of Health for year 2016. The method of analysis is
Spatial Autoregressive Model (SAR). The results are the prevalence of leprosy in an area
is influenced by the prevalence of leprosy in neighboring districts, the percentage of BCG
vaccines recipient and the percentage of households with healthy lifestyle. SAR model fit
the data well.
Keywords: Leprosy, South Sulawesi, SAR

References[1] Anies. (2006). Manajemen Berbasis Lingkungan Solusi Mencegah dan Menanggulangi
Penyakit Menular. Jakarta: PT. Elex Media Komputind.
[2] Anselin, Luc and A. Bera. (1998). Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics. Handbook of Applied Economic
Statistics, 237–289. New York: Marcel Dekker.
[3] Bustaman et al. (2013). Penggunaan Metode GWR untuk Analisis Data Sosial dan
Ekonomi. Jakarta: Badan Pusat Statistik.
[4] Devy. dkk. (2014). Geographically Weighted Poisson Regression (GWPR) Untuk Pemodelan Jumlah Penderita Kusta Di Jawa Tengah. Statistika. Vol.2, No.2.
[5] Dzikrina, Aliefa Maulidia & Purnami, Santi Wulan. (2013). Pemodelan Angka
Prevalensi Kusta dan Faktor yang Mempengaruhi di Jawa Timur dengan Pendekatan
Geographically Weighted Regression (GWR). Jurnal Sains Dan Seni Pomits, Vol. 2,
No. 2.
[6] World Health Organization. (2014). Weekly epidemiological record: Global leprosy
update (need for early case detection). Geneva:WHO.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Spatial Modeling of Ground-Level PM2.5 in Taiwan
Based on Two Types of Data
Chi-Wei Laia , Hsin-Cheng Huangb and Sheng-Li Tzengc
a Institute of Statistical Science, Academia Sinica,b Institute of Statistical Science, Academia Sinica,c Department of Applied Mathematics, National Sun Yat-sen University

There are two systems to monitor fine particulate matter (PM2:5) in Taiwan. One consistsof 77 monitoring stations of the Environmental Protection Administration, which provides
high-quality measurements. The other one involves a large number of low-cost internet-ofthings devices called AirBoxes, which produce less precise measurements but with muchbroader coverage. In this research, we propose a spatial model based on multi-resolution
spline and kriging to obtain spatial prdiction at any location in Taiwan by combining
these two types of data. In addition, we develop a Shiny application that automatically
identifies unusual measurements and shows the current PM2.5 concentration map withuncertainty quantification based on the proposed method.
Keywords: Fine particulate matter, Kriging, Regression calibration.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

A spatial rank-based EWMA chart for monitoring linear proles
Longcheen Huwanga , Jian-Chi Lina and Li-Wei Lina
a Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan

Profile monitoring has been recently considered as one of the most promising areas ofresearch in statistical process monitoring (SPM). It is a technique for monitoring thestability of a functional relationship between a dependent variable and one or more independent variables over time. The monitoring of linear profiles is the most popular one
because the relationship between the dependent variable and the independent variables iseasy to describe by linearity, in addition to its exibility and simplicity. Furthermore, almost all existing charting schemes for monitoring linear profiles assume that error terms
are normally distributed. In some applications, however, the normality assumption oferror terms is not justified. This makes the existing charting schemes not only inappropriate but also less eficient for monitoring linear profiles. In this article, based on thespatial rank-based regression, we propose a charting method for monitoring linear profiles where the error terms are not normally distributed. The charting scheme applies
the exponentially weighted moving average (EWMA) to the spatial rank of the vectorof the Wilcoxon-type rank-based estimators of regression coeficients and a transformed
error variance estimator. Performance properties of the proposed charting scheme are
evaluated and compared with an existing charting method based on multivariate sign interms of the in-control (IC) and out-of-control (OC) average run length (ARL). finally, areal example is used to demonstrate the applicability and implementation of the proposed
charting scheme.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Crime prediction of street harassment
T. Ohyama, and M. Amemiya
University of Tsukuba

Street harassment, such as sexual comment, public indecency or sexual touching reduce
our quality of life as well as lead to serious sexual assaults [1]. Crime prediction, which has
been used to predict crime occurrences by statistical algorithm, is promising to prevent
street harassment; nevertheless, it had never been applied to predict these nuisances. On
the other hand, existing crime prediction methods, which set grid cells on the city, result on relatively broad areal prediction. However, it is insufficient for prevention efforts.
Fine level prediction, such as at streets or places is needed to allocate prevention resources.
This study tries crime prediction of street harassment in Fukuoka, Japan using disaggregate logit model. The prediction model assumes crime generators and crime enablers[2]. The former corresponds to the likelihood of potential victims traffic and the latter tothe likelihood of guardians absence. Where these factors converge can be crime-attracting
streets or places. Referring environmental factors, we identify these spots which call harassment. Moreover, interactions of offences are considered in the model. It is known thatsimilar offences are clustered within short distance and time period [3]. Based on thisidea, calculated risk is weighted by the latest offences. The prediction result is verified bycomparing actual occurrences and the validity of prediction model is discussed.
Keywords: Crime prediction, Street harassment, Interaction of crime.

References[1] G. Kikuchi, M. Amemiya, T. Shimada, T. Saito, and Y. Harada (2009). A SpatioTemporal Analysis of Contacts by Suspicious Persons and Subsequent Occurrence of
Sex Crimes. Japanese Journal of Sociological Criminology, 34, 151-163. (in Japanese)[2] Clarke, R., and Eck, J. (2003) Become a problem-solving crime analyst: In 55 small
steps. Jill Dando Institute of Crime Science. London: Ashford Colour Press.
[3] Townsley, M., Homel, R., and Chaseling, J. (2003) Infectious burglaries. A test of
the near repeat hypothesis. British Journal of Criminology, 43(3), 615-633.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4Multi Compound Option for Bond Performance with
Jump Diusion Process
D.A.I Maruddania and Abdurakhmanba Diponegoro University, b Gadjah Mada University

Credit risk theory for bond valuation is conventionally expressed as zero coupon bond andassumes normal distribution in the logarithmic returns of asset prices. Those assumptions
have been widely used in the Black-Scholes-Merton bond framework to model the returnof assets. In real bond trading, the most common form of debt is coupon bond andthe logarithmic returns of asset prices are not normally distributed. The characteristics
of asset prices commonly contain jump processes. This paper study the valuation of nperiods coupon bond where the asset price process contains a compound Poisson jumpcomponent, in addition to a continuous log-normally distributed component. We deriveclosed-form solution for equity and default probability of the bond which gives coupon
for n-periods using straightforward integration.
Keywords: Jump diusion processes, Compound Poisson, Extreme value assets.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Visual analytics of a large longitudinal survey data
D. Danardonoa
a Department of Mathematics, Universitas Gadjah Mada

Large scale population based longitudinal surveys offer an opportunity to understand factors and dynamic behavior of units in the surveys in relation to many interesting outcomessuch as socio-economic status and health-related events. Recent longitudinal surveys
in Indonesia [1], the IFLS (Indonesia Family Life Survey), collected extensive measuresof health status, including self reported measures of general health status, symptoms,
pain, doctor diagnosed chronic conditions, time spent on different physical activities andbiomarker measurements together with information on social and economic conditions of
the individuals and households, as well as information on community facilities. However,the data are quite complex. There are five panels in 1993, 1997, 2000, 2007 and 2014
involving more than 16 thousands households and 80 thousands individuals. The data
are stored in about 270 files. Although it can be constructed as a relational database,the large number of topics and variables in the database may prevent scholars and policymakers in exploring and utilizing the data efficiently. Visual analytics [2] may help IFLS’s
users to explore the characteristics, factors and outcomes in the IFLS data conveniently.
Although users or scholars usually already have research questions in mind, exploring thedata may give a better understanding and may even generate new research questions or
generating hypothesis. Visual analytics helps users to gain hindsight and insight of thedata. To implement visual analytics of IFLS data, a relational database were constructed
and computing tools based on R software[3] to facilitate important visualization methods
for depicting composition, distributions, relationships and comparison [4] in the form of
profile plot for longitudinal measurements and time series plot for household involvementin the surveys were developed. Some simple data analytic methods were also provided.
Keywords: health-related events, longitudinal survey, visualization.

References[1] J. Strauss J, F. Witoelar F and B. Sikoki (2016). The fifth wave of the Indonesian
Family Life Survey (IFLS5): Overview and Field Report, RAND, Santa Monica[2] J. J. Thomas and K. A. Cook (2005). Illuminating the Path: The Research andDevelopment Agenda for Visual Analytics, IEEE Press.
[3] R Core Team (2018). R: A Language and Environment for Statistical Computing,
Vienna, Austria, https://www.R-project.org/[4] Ivo D. Dinov (2018) Data Science and Predictive Analytics: Biomedical and HealthApplications Using R, Springer.

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Poster: Tuesday 13 (14:30-15:50) room Lucent plaza, page: 4

Statistical modeling of logarithmic diﬀusions in word
count time series in nation-wide language data sets
H. Watanabea,ba Kanazawa university, b Institute of Statistical Mathematics

Languages are one of typical complex systems, which have the well known languageindependent statistical laws such as Zipf’s and Heap’s. In this study, we investigate thedynamical statistical properties in languages by using massive nation-wide databases related to word usage that has developed in the past 10 years. Especially, we focus on
the stability or the slowness of change in the usage of already popular words from theviewpoint of diﬀusion on a complex system and show that common logarithmic diﬀusion(i.e. very slow diﬀusion or change) is approximately observed by some languages or media[1].
The logarithmic diﬀusion (which is called as “ultraslow diﬀusion” in physics), i.e. thediﬀusion characterized by the logarithmic function, has been extensively studied theoretically in physics, but has hardly been observed empirically. In this study, we find theultraslow-like diﬀusion of the time-series of key-word count of already popular words on
three diﬀerent nationwide language databases: (i) newspaper articles (Japanese), (ii) blog
articles (Japanese), and (iii) page views of Wikipedia articles (English, French, Chinese,
and Japanese). In addition, by mathematical modeling, we show that this diﬀusion isbasically explained by the random walk model with the power law forgetting with theexponent β ≈ 0.5, which is related to the fractional Langevin equation (or the autoregressive fractionally integrated moving average model). The exponent β characterises speedof forgetting and β ≈ 0.5 is corresponding to (i)the border (or thresholds) between thestationary and the nonstationary and (ii)the right in the middle dynamics between the
IID noise for β = 1 and the normal random walk for β = 0.
The key-word count time series (ex. daily time series of word appearances for the focus
keyword on nationwide Japanese blogs), which we studied, is mostly used to measure temporal changes in social concerns related to the considered word in both basic sciences andpractical applications. The properties, which we found, are basically common in various
time series of key-word count independently of languages and medias (i.e., newspapers,
blogs etc.). Thus, it is expected that our results will help us to precisely observe humanbehaviors using the key-word count time series widely, in particular, extracting essential
information from noisy time series data.
Keywords Languages statistics (Online social media data), Long memory time series,
Poisson point process

References[1] H. Watanabe (2018). Empirical observations of ultraslow diﬀusion driven by thefractional dynamics in languages. Physical reveiew E, 98, 012308.

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CPS_7: Tuesday 13 (14:30-15:50) room RY102, page: 7

Mean residual life function estimators for nonnegative
data by logarithmic transformation
Rizky Reza Fauzia and Yoshihiko Maesonob
a Graduate School of Mathematics Kyushu University, b Faculty of Science and Engineering Chuo

University

In this talk we discuss the kernel-type estimators of the mean residual life functionmX (t) = E(X − t|X > t). New estimators that can eliminate the boundary effect areproposed. Let X1 , X2 , ..., Xn be independently and identically distributed nonnegative
random variables with
R ∞an absolutely continuous survival function SX and a density fX .
If we define SX (t) = t SX (x)dx, then the mean residual life function can be written asmX (t) = SX (t)[SX (t)]−1 .
The naive kernel estimator of mX (t) has been discussed by [1], defined as mb X (t) =−1bb
SX (t)[SX (t)] , where




n
n
t − Xit − XihX
1X
bbV, SX (t) =V,(1)
SX (t) =
n i=1
h
n i=1
h
R∞
R∞
with V (x) = x K(y)dy and V(x) = x V (y)dy. The function K(y) is a kernel function
and h > 0 is a bandwidth. Even though under some weak regularity conditions the naive
kernel estimator performs well, the boundary bias problem arises when the support of thedata is not the entire real line.
Our purposal is to eliminate the boundary bias problem using two new kernel-type
estimators. The proposed estimators are, with j = 1, 2, m
e X,j (t) = e
SX,j (t)[SeX,j (t)]−1 ,P
P
SX,j (t) = n1 ni=1 Vj,h (log x, log Xi ), with
where SeX,j (t) = n1 ni=1 Vj,h (log x, log Xi ) and e




Z y
x−y
x−z
x+y
−z
V1,h (x, y) = V, V1,h (x, y) = e
e V
dz,
(2)
h
h
−∞
and
1
V2,h (x, y) =
h

Z y


K

−∞

x−z
h

Z y


dz,

V2,h (x, y) =

z

eV
−∞



x−z
h


dz.

(3)

Because no weight is put on the negative real line by the logarithm function, there is noboundary effect in our estimators. Though the formulas seem complicated, the intuition
behind this method is quite simple. The functions Vj,h (log t, log Xi ), j = 1, 2, are actually related to the integration of Vj,h (log t, log Xi ), respectively, preserving the relation
between SX (t) and SX (t) in the formula of mX (t).
Keywords: Boundary bias problem, Kernel estimator, Mean residual life function.

References[1] A. Guillamón, J. Navarro, and J. M. Ruiz (1998). Nonparametric estimator for mean
residual life and vitality function. Statistical Papers, 39, 263–276.

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CPS_7: Tuesday 13 (14:30-15:50) room RY102, page: 7

Prediction of model mean using double power additivetransformation
Takanori Yoshikawaa , Yoshimichi Ochib and Masashi Gotoc
a Wakayama medical university hospital, b Oita university, c Biostatistical research association

In the inference of a linear model, normality of observation values, additivity of the model,
homoscedasticity of error, and independence of observation values are generally assumed.
The data obtained in the clinical evaluations, however, rarely meet all these assumptions,and there are situations where it is effective to transform the observed values. Box & Cox
transformation (Box & Cox, 1964) has been proposed to fulfill these assumptions. Also,
as an extension, Double Power Additive Transformation (DPAT) is proposed, which aims
to achieve additivity of the model and homoscedasticity of error simultaneously, assuming
the normality of the observed values (Goto, 1992). In this paper, we propose an alternativetransformation model, referred to as DPAT2 model, and a simultaneous confidence bandof the model mean using the proposed transformation model.
DPAT2 model assumes that y(λ1 ) obtained by Box-Cox transformation of y with transformation parameter λ1 follows the heteroscedastic linear regression model, and y(λ1 , λ2 )obtained by additional Box-Cox transformation of the y(λ1 ) with the transformation parameter λ2 achieves the homoscedasticity. We firstly discuss the model definition and theparameter estimation.
Secondly, we consider a simultaneous confidence band of the model mean of the original response y using the DPAT2 model. We firstly construct a simultaneous confidenceband for y(λ1 ) according to Scheffé (1960) with variance-covariance matrix of regression
parameters, conditioning on parameters other than regression parameters. The simultaneous confidence band of y is derived by the inverse transformation of the Box & Cox
transformation of y(λ1 ) with transformation parameter λ1 .
We evaluate the performance of the constructed simultaneous confidence bands w.r.t.
coverage probabilities by simulation. In the case of λ1 = 1, the coverage probability wasclose to the nominal level. On the other hand, in the case of λ1 = 0.5, some decrease fromthe nominal level was observed.
Keywords: Box-Cox transformation, non-linear regression, heteroscedasticity.

References[1] Box, G. E. P. & Cox, D. R.(1964). An analysis of transformation(with discussion).
J. Roy. Statist. Soc., B26, 211-243.
[2] Goto, M.(1992). Extensive views of power transformation: Some recent developments. Invited paper at Honolulu Conference on Computational Statistics as a memorial of the fifth anniversary of JSCS, JAIMS, December 1-5.
[3] Scheffé, H. (1960). The analysis of variance. Wiley.

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CPS_7: Tuesday 13 (14:30-15:50) room RY102, page: 7

Inference about Binocular Sensitivity and Specificityof Screening Tests for Paired Organs
Tsung-Shan Tsouaa Institute of Statistics, National Central University, Taiwan

Recently Perera, et. al. (2017) introduced two new binocular accuracy measures to evaluate diagnostic tests for paired organs. They adopted the extended common correlation
model and Gaussian copula model to account for correlation between fellow eyes. As
the measures are functions of several joint probabilities and due to the complexity of thejoint models, the variation of the two parameter estimates would need to be acquired via
bootstrapping.
In our opinion, correlation is irrelevant to inference for the two binocular accuracy measures. Hence, joint models such as the two just mentioned are not needed. We suggestthat when patients are independent, the binomial models suﬃce for inference about theparameters of interest. We perform numerical studies with the same simulation settings
and analyze the data set as of Perera, et. al. (2017) for illustration.
Keywords: Pairing, Binocular sensitivity, Binocular specificity

References[1] Perera Y, Ren M, Punzalan J, Rudnisky C, de Leon A. Binocular sensitivity andspecificity ofscreening tests in cross-sectional diagnostic studies of paired organs.
Statistics in Medicine, 36, 1754–1766.

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CPS_8: Tuesday 13 (14:30-15:50) room RY105, page: 7

Principal component analysis using
the Gini coeﬃcient penalty function
J. Tsuchidaa and H. Yadohisaba Tokyo University of Science, b Doshisha University

Principal component analysis (PCA) is one of the most popular dimensional reduction
methods. PCA is often used for data exploration, in which interpretation of PCA results
has an important role. Recent years have witnessed the emergence of data comprisingvoluminous objects and variables in many research areas. Although we often analyze suchdata, the PCA results obtained from them are diﬃcult to interpret because of the large
number of estimated values and their interrelationships that need to be checked. This
problem can be solved by sparse estimation, such as using the least absolute shrinkage andselection operator (LASSO) [3] for loadings [5], and maximization of the (co)variance of
estimated loadings, for example, based on the Varimax criterion [2]. Although we often use
either of these solutions for PCA, they are applied simultaneously for easy interpretation.
To overcome this issue, we propose a PCA method with the Gini coeﬃcient used as apenalty function. The Gini coeﬃcient is the ratio of the mean absolute diﬀerence tothe ℓ1 norm [1]. Therefore, maximizing the Gini coeﬃcient amounts to maximizing thesparsity and variance simultaneously. Although the Gini coeﬃcient has good properties,
it is nonconvex. To address this, we introduce a majorizing function for the objectivefunction and use local linear approximation [4]. The results of the numerical study showthat the mean squared error of loadings and mean of variance for the estimated value aresuperior to LASSO-type penalties.
Keywords: Alternative least squares, Dimensional reduction, Sparse estimation.

References[1] Hurley, N. (2009). Comparing measures of sparsity. IEEE Transactions on Information Theory, 55, 4723–4741.
[2] Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis.
Psychometrika, 23 , 187–200.
[3] Thibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal ofRoyal Statistics Society Series B, 58 , 267–288.
[4] Zou, H. and Li, R. (2008). One-step sparse estimates in nonconcave penalized likelihood models. The Annals of Statistics, 36, 1509–1533.
[5] Zou, H., Hastie, T., and Tibshirani, R. (2006). Sparse principal component analysis.
Journal of computational and graphical statistics, 15, 265–286.

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CPS_8: Tuesday 13 (14:30-15:50) room RY105, page: 7

Implementing a biplot based multivariate data driven
industrial performance index
N. J. Le Rouxa , R. F. Rossouwb and R. J. L. Coetzerb
a Department of Statistics and Actuarial Science, Stellenbosch University, South Africa, b Sasol

Group Technology R&T, Sasol, South Africa

The Sasol Coal Gasification (SCG) plant in South Africa is a highly complex chemical
facility. The continuous improvement of the SCG plant is of critical importance to thecompany to ensure a stable supply of high quality gas to the downstream units. Therefore, in order to maintain optimum product yields and to sustain throughput, an efficient
multivariate process monitoring methodology is required for all the reactors or gasifiers.
This monitoring methodology entails establishing for each individual gasifier a real time
gasifier performance index (GPI). This GPI should provide a single value which indicatesthe current health of the gasifier.
Three different approaches were identified to address this challenge. First, a fundamental
approach followed by process engineers to develop an index consisting of a weighted deviation from a recommended operating point for each process variable. Second, a purely
data driven (empirical) approach to develop a performance index making use of historicalmultivariate data collected during periods of good performance. Third, the integration of
the fundamental and empirical indices to develop a GPI index which uses both the multivariate statistical methodology, as well as the knowledge from subject matter experts.
In this paper only the development and implementation of the multivariate data driven
GPI is considered. Furthermore, Coetzer et al. (2014) discuss the use of generalized
orthogonal Procrustes analysis to select a reference set for the multivariate monitoring ofmultiple production processes simultaneously. Therefore, given a period where the wholeindustrial process is in control, the following aspects are addressed: The selection of the
optimal principal component combinations to utilize for possible biplot scaffoldings orvisualizations. The optimal set of axes to include for each principal component combination for purposes of visualization. The identification of critical variables responsible forprocess performance deviations for each principal component combination presented.
Keywords: Biplots, Process Monitoring, Variable Contribution

References[1] R. L. J. Coetzer, R. F. Rossouw, and N. J. Le Roux (2014). Reference set selection
with generalized orthogonal Procrustes analysis for multivariate statistical processmonitoring of multiple production processes. Chemometrics and Intelligent Laboratory Systems, 132, 52–62.

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CPS_8: Tuesday 13 (14:30-15:50) room RY105, page: 7

Cross-validation error in classification with dimension
reduction and applications to proteomics mass spectrometry imaging
Inge Kocha and Lyron Winderbaumba University of Western Australia, b University of Adelaide

Cross-validation (CV) has become an established tool for assessing and comparing classification methods. The classification and prediction of high-dimension low sample size
(HDLSS) data require dimension reduction (DR), and generalisation of CV to classification with DR is not only not unique but the CV errors vary considerably across thegeneralisations. Reproducible research requires specification of the detailed steps of one’s
approach.
We exhibit four generalisations of CV to classification with DR. To reduce complexity, we
assume that m-fold CV has been fixed. The four generalisations are characterised by theorder in which the DR transformation and selection of the CV subsets occurs, and dependon how the CV error is calculated: choosing the dimension first and calculating the error
over all CV testing sets, or the other way round. We state the precise form of the four
approaches, give an expression of the CV error and the optimal number of dimensions.
We illustrate these ideas using linear discrimination with principal components and canonical correlation-based variable ranking (see Sections 3.2, 4.8 and 13.3 of [2]), but otherclassification and DR approaches can be used. We apply the four CV variants to simulated data and real proteomics mass spectrometry imaging data from 47 patients withendometrial cancer that metastasise in 16 patients (see [1] and [3]). The analyses showthat the four approaches result in different predication errors and optimal numbers ofdimensions. Typically DR by variable ranking outperforms principal components. For
the simulated data our results clearly show the difference in performance and hence theneed for precise formulation of the approaches in order to guarantee reproducibility.
Keywords: Cross-validation error, Optimal dimension reduction, Proteomics MSI cancer
data.

References[1] S. Bendifallah, A. S. Genin, I. Naoura, N. C. Buffet, F. C. Chapelon, B. Haddad,... and M. Koskas (2012). A nomogram for predicting lymph node metastasis ofpresumed stage I and II endometrial cancer. American Journal of Obstetrics andGynecology, 207(3), 197-e1.
[2] I. Koch (2013) Analysis of Multivariate and High-Dimensional Data. Cambridge University Press.
[3] L. Winderbaum, I. Koch, P. Mittal and P. Hoffmann (2016). Classification of MALDIIMS imaging data of tissue microarrays using canonical correlation analysisbased
variable selection. Proteomics, 16(11-12), 1731-1735.

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CPS_8: Tuesday 13 (14:30-15:50) room RY105, page: 7

Sufficient Reduction in Large Ensembles of Time Series Analysis
Heng-Hui Lue
Tunghai University

In this paper we propose parsimonious models for analyzing large ensembles of time series.
The idea is to decompose each series in the ensembles as a weighted sum of a small number of component series plus a stochastic error. It aims to search for leading directionsin the variations of autocorrelation sequences to achieve the sufficient reduction of large
ensembles of time series. We demonstrate the efficacy of our method for large ensemblesof time series data sets through several simulations and real data sets.
Keywords: Dimension reduction, Multivariate time series, Time series decomposition.

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ITS_2: Tuesday 13 (16:20-17:20) room RY101, page: 7

Nonparametric Inference for Activity Profiles from Wearable Devices
Ian McKeaguea
a Columbia University, New York, USA

This talk introduces a nonparametric framework for analyzing physiological sensor datacollected from wearable devices. The idea is to apply the stochastic process notion ofoccupation times to construct activity profiles, circumventing the problem of pre-aligning
the sensor data. A nonparametric likelihood ratio approach is developed, making eﬃcientuse of the activity profiles to provide a confidence band for their means, along with an
ANOVA type test. These procedures are calibrated using bootstrap resampling. The
proposed methods apply, more generally, to monotonic functional data, but have theadvantage (over generic functional data methods) that smoothing techniques are avoided.
Accelerometer data from subjects in a U.S. National Health and Nutrition Examination
Survey (NHANES) are used to illustrate the approach. The talk is based on joint workwith Hsin-wen Chang (Academia Sinica, Taipei).

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ITS_2: Tuesday 13 (16:20-17:20) room RY101, page: 7

Signal estimation from large ranking data sets: challenges and strategies
M. G. Schimeka and L. Vitaleaba Medical University of Graz, Austria, b University of Salerno, Italy

Recent years have seen an increasing interest in rank-based statistical methods to meetthe requirements of new technologies producing huge amounts of data. In traditional
applications such as the rating of consumer products or of political parties, the numberof ranked items is low but in current applications we are confronted with thousands ofitems. As additional challenge, the number p of items often exceeds the number n of
rankers (human experts or machines). Let us assume a set of p distinct items ordered
by n independent rankers, represented in a matrix of ranks. In most applications, theunderlying signals or decision processes cannot be observed. The ultimate goal is toestimate the unknown signals causal for the observed rankings.
Recently proposed methods rely on Markov chain Monte Carlo techniques, for instance[2] and [3]. Even when optimally implemented, the computational demand of stochastic
optimization is too high to allow for hundreds or even thousands of ranked items. Here we
introduce and discuss alternative approaches that make signal estimation feasible for large
or even huge sets of items. We take an indirect inference approach [1] for the analysis of
the empirical matrix of ranks. However, to avoid heavy computations, the generic signals
are now recovered via minimization of the noise under various objective functions. For
additional computational efficiency, the number of constraints of a simple signal plus noisemodel can be reduced to n(p − 1) in a convex optimisation approach. As a byproduct,
conventional rank aggregation results can be obtained from the signal estimates. Finally,
simulation and bootstrap evidence for different combinations of n and p is reported.
Keywords: Ranking Data, Linear (Quadratic) Programming, Indirect Inference.

References[1] K. Heggland, and A. Frigessi (2004). Estimating functions in indirect inference. Journal of the Royal Statistical Society: Series B, 66(2), 447-462.
[2] V. Svendova, and M. G. Schimek (2017). A novel method for estimating the common
signals for consensus across multiple ranked lists. Computational Statistics and Data
Analysis, 115(4), 122–135.
[3] V. Vitelli et al. (2018). Probabilistic preference learning with the Mallows rank model.
Journal of Machine Learning Research, 18(4), 1–49.

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CPS_9: Tuesday 13 (16:20-17:20) room RY102, page: 8

Measuring unobserved cultural eﬀects on music trade:grouping interactive ﬁxed eﬀects in sample selection
modelsS. Takagia , and Y. Takaraba Hokkaido University, b Chiba Keizai University

This study measures the eﬀect of cultural relations on trade in cultural goods, speciﬁcally,
music compact discs (CDs). To introduce cultural relations to our estimation modelof trade, we include multiple interaction terms of ﬁxed eﬀects (e.g. [1]) as proxies forunobserved cultural relations. Each interaction term is a multiplicative one of ﬁxed eﬀects
speciﬁc to an importer and an exporter after controlling traditional variables and factors
used in the trade literature (e.g. [2] and [3]) that successfully account for the economic
statuses of two countries and trading costs between them. The major unexplained partof the cultural goods trade is attributable to cultural relations between the countries, andthe interactive terms can reﬂect the eﬀect of the relations on trade.
Empirical implications on unobserved eﬀects are elicited from the estimated ﬁxed eﬀects
speciﬁc to each country. As a result, the number of parameters is proportional to thesample size. Although the estimates are proved to be consistent and have empiricalcontents, the results are too complicated to be interpreted by just looking at the numbers.
To clarify interpretations of the estimated ﬁxed eﬀects, we propose an model estimation
method, based on an expectation conditional maximization (ECM) algorithm, with aroutine of classifying the ﬁxed eﬀect parameters into a smaller number of groups.
The contributions of this study are as follows: (i) we develop a statistical model that incorporates the grouped-additive and grouped-interactive terms of ﬁxed eﬀects with sample
selectivity to make consideration of the presence of zero trade paths, (ii) the asymmetricand unobserved cultural relations between countries are quantitatively measured, and (iii)novel world maps and ﬁgures on cultural relations are visualized.
Keywords: Interactive ﬁxed eﬀects, ECM algorithm, Cultural goods trade.

References[1] J. Bai, and K. Li (2014).Theory and Methods of Panel Data Models with InteractiveEﬀects, Annals of Statistics, 42(1), 142–170.
[2] E. Helpman, M. Melitz, and Y. Rubinstein (2008). Estimating Trade Flows: Trading
Partners and Trading Volumes, Quarterly Journal of Economics, 123(2), 441–487.
[3] Y. Takara (2018). Do Cultural Diﬀerences Aﬀect the Trade of Cultural Goods? A
Study in Trade of Music, Journal of Cultural Economics, 42(3), 393–417.

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CPS_9: Tuesday 13 (16:20-17:20) room RY102, page: 8

From Docker to Kubernetes: Scaling Machine
Learning Algorithms
E. J. Harnera and M. Lilbackb
a West Virginia University, b Rc2 ai

Rc2 (R Cloud Computing) is a collection of Docker containers for running R and Sparkwith various persistent data stores, including PostgreSQL, HDFS (Hadoop Distributed
File System), Hive, etc. The core components of Rc2 are an app server, a database server,
and 0 or more R-based compute servers. The user interacts with the app server through anative Rc2 client. Currently, the client supports R markdown documents and notebooks.
Rc2 can be run in single-user mode (running on a local machine or server) or in multi-user
mode, typically run on a scalable cloud service.
Kubernetes orchestrates these sets of running Docker containers and volumes, called pods,
which are the smallest deployable unit within a cluster. Applications running in the same
pod share the same IP address, the same port space, and the same namespace. Althoughdeployment of pods can be done in different ways, e.g., as a traditional high-performancecomputing environment, the focus here is in scaling the number of Rc2 users.
Rc2 uses Ingress for managing access by multiple users to the various supported services.
This involves user authentication and the spawning of requested Rc2 services. The canonical Rc2 service is a pod running R and other tightly coupled applications, e.g., Java. Other
services, e.g., PostgreSQL and Spark, are shared, stateful services and are made available
to the user depending on the login request and the user’s permissions. Kubernetes not
only ensures that the dependencies among and within the requested pods are enforced,
but also that the number of available pods always exceeds the number of running podsby a specified number.
The power and flexibility of the Rc2 architecture is illustrated with several machine learning examples, which are available here: https://github.com/jharner/DSSV2019Rc2.
Keywords: R, Spark, Docker containers, Kubernetes, Ingress.

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CPS_9: Tuesday 13 (16:20-17:20) room RY102, page: 8

Exponential convergence of stochastic gradient descentfor binary classification problems
Atsushi Nitandaa,b and Taiji Suzukia,ba The University of Tokyo, b RIKEN AIP

The goal of binary classification problems is to find the Bayes classifier that minimizes
the expected classification error over all measurable functions. To this end, we solve theexpected risk minimization problems defined by convex surrogate loss functions such as
logistic loss and exponential loss, instead of directly minimizing the classification error.
This approximation is theoretically supported by the consistency property of loss functions, which connects an excess surrogate loss and an excess classification error.
Stochastic gradient descent is the most commonly used method for expected risk minimization problems thanks to its several advantages (fine scalability, simplicity of implementation, and superior performance). Hence, there is a great deal of research into
analyzing its √convergence behavior. As for the convergence rate, asymptotically optimal
rates of O(1/ T ) and O(1/T ), where T is the number of iterations, were obtained for thegeneral convex and strongly convex problems. Therefore, convergence rates of classification errors can be also derived from these rates with the consistency property of surrogate
loss functions and can be accelerated by preferable assumptions such as the low-noise
condition, but obtained rates are still sublinear because classification errors are generally
slower than those of surrogate loss functions.
Therefore, it is interesting whether much faster convergence of classification errors can beachievable by its direct analysis without the consistency property. Recently, an exponential convergence rate of O(exp(−T )) for stochastic gradient descent was shown [3] under
the strongest version of low-noise condition [1, 2] for the squared loss function, which issomewhat inadequate for binary classification tasks. In this talk, we present our work [4]that also shows an exponential convergence under similar assumptions in the final phaseof the stochastic gradient descent for a wide class of differentiable convex loss functions
including the logistic loss. In addition, for the averaged stochastic gradient descent, weshow that the same convergence rate holds from the early phase of training.
Keywords: Stochastic Gradient Descent, Low Noise Condition, Exponential Convergence Rate.

References[1] J-Y. Audibert and A. B. Tsybakov. (2007). Fast learning rates for plug-in classifiers. The Annals of
statistics, 35(2), 608–633.
[2] V. Koltchinskii and O. Beznosova. (2005). Exponential convergence rates in classification. In International Conference on Computational Learning Theory.
[3] L. Pillaud-Vivien, A. Rudi, and F. Bach. (2018). Exponential convergence of testing error for stochastic gradient methods. In Conference on Learning Theory.
[4] A. Nitanda and T. Suzuki. (2019). Stochastic gradient descent with exponential convergence rates
of expected classification errors. In International Conference on Artificial Intelligence and Statistics.

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CPS_10: Tuesday 13 (16:20-17:20) room RY105, page: 8

A Bayesian nonparametric topic model Using an Outcome Variable for Microbiome Data
T Okuiaa Medical Information Center, Kyushu Univeristy Hospital

Using next-generation sequencing, 16S rRNA gene sequences can be obtained relatively
easily nowadays. A common aim of studies that use 16S rRNA sequencing microbiome
data is to link human microbiomes with a clinical outcome such as a disease.
The latent Dirichlet allocation (LDA) model has often been used in the natural language
processing domain for extracting latent topics comprising words from documents. LDA
has been proposed for extracting microbial clusters in the analysis of sequencing data. The
LDA models that use supervisory information such as the characteristics of documents
have already been proposed in the natural language processing domain; for example, supervised topic models [1] or topic models conditioned on arbitrary features using Dirichlet
multinomial regression (DMR) [2] have been reported. However, these methods have notbeen applied to microbiome data. Moreover, when LDAs are used for microbiome data,
each extracted topic may represent a relatively small percentage of topics, whereas topics
that account for a large percentage of topics are usually not extracted.
Therefore, we propose a Bayesian nonparametric topic model that is conditioned on arbitrary features by DMR, which uses the stick-breaking process for generating topic distributions. We compared the predictive performance of this model with those of the existingmodels using two real microbiome datasets. Our model’s ability to predict the outcomeof a selected variable was superior to that of the other models. Moreover, in the proposed
method, higher association of a topic with a clinical outcome corresponded to larger proportion of the topic. Therefore, the proposed method can identify microbial topics thatare more associated with a clinical outcome.
Keywords: Microbiome data, Latent dirichlet allocation, Nonparametric bayes

References[1] D. Blei and J.D. McAuliﬀe (2007). Supervised topic models. In Advances in Neural
Information Processing Systems (NIPS). J.C. Platt, D. Koller, Y. Singer, and S.
Roweis, Cambridge, MA.
[2] D. Mimno and A. McCallum (2008). Topic models conditioned on arbitrary features with Dirichlet multinomial regression. In Uncertainty in Articial Intelligence,
Helsinki, 411–418.

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CPS_10: Tuesday 13 (16:20-17:20) room RY105, page: 8

Sample sizes and properties of four Bayesian designs for Proofof-Concept trials
Fanghong Zhang Novartis Pharma K.K.
Bayesian designs have been widely used in clinical trials, especially for the proof-of-concept (PoC)
trials. The primary objective of PoC trial is to make Go (move to next phase of development) or
No Go (stop development) decision based on the observed eﬃcacy data. In oncology setting, theprimary eﬃcacy endpoint is usually the response rate. Bayesian approach combines prior beliefs
with the evidence from the current trial to make decisions. When planning a Bayesian trial,
we get prior information, and need to know the amount of additional evidence from the datathat enables us to take a decision. The required amount of additional evidence from the data isexpressed by sample size and the number of responses in PoC trials. Several Bayesian designs
with diﬀerent concepts are proposed. However, it may be diﬃcult to choose the appropriate
Bayesian design from them since no comprehensive comparisons are provided yet. Due to this,
we are going to show that four Bayesian designs use diﬀerent way to define the required amountof additional evidence from data. We discuss some properties of the designs to show the behaviorof sample size for a range of design parameters. In addition, we will investigate their frequentist
operating characteristics with the prior varies. For Bayesian design in PoC trials, the underlying
statistical problem is essentially testing a one-sided hypothesis for a response rate H0 : p =p0 versus H1 : p > p0 based on observation of a number of responses X = x out of n subjects.
Bayesian criteria for decision making in clinical trials have been discussed by [3], [4], [1] and[2]. Following [4], let α and 1 − β be the Bayesian Type I error and power. The four designs
use one common criterion that is P (p > p0 |n, x = u) ≥ 1 − α, to show the eﬃcacy. Given
same prior, there are many combinations of (n, x) which satisfy the eﬃcacy criterion. [3] defies
the required amount of evidence in a way with varying number of responses given same sample
size. In contrast, [1] defies the required evidence in a way with varying sample size given thesame number of responses. On the other hand, [2] defies the required evidence in a way keeping
a relationship between sample size and number of responses. Finally, [4] uses a frequentist
method essentially. We will investigate the minimum sample size and the frequentist operating
characteristics with various design parameters for the four Bayesian designs using an unified
notations. This will be helpful for user to choose appropriate Bayesian design and calculate
appropriate sample size to meet their development strategy.
Keywords: Bayesian sample size, operating characteristics, proof-of-concept trial.

References[1] Gsponer T, Gerber F, Bornkamp B, Ohlssen D, Vandemeulebroecke M, and Schmidli H(2014). A practical guide to Bayesian group sequential designs. Biopharm Stat., 13, 71-80.
[2] Tan S-B and Machin D (2002). Bayesian two-stage designs for phase II clinical trials. Stat.
Med., 21, 1991-2012.
[3] Whitehead, J., Valdes-Marquez, E., Johnson, P. and Graham, G. (2008). Bayesian sample
size for exploratory clinical trials incorporating historical data. Stat. Med., 27, 2307-2327.
[4] Zaslavsky, B.G. (2012). Bayesian Sample Size Estimates for One Sample Test in Clinical
Trials With Dichotomous and Countable Outcomes. Stat. Biopharm. Research, 4, 76-85.

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CPS_10: Tuesday 13 (16:20-17:20) room RY105, page: 8

On the determination of number of bases in Bayesiannonnegative matrix factorization using the marginal
likelihood
H. AbeKyoto University

Nonnegative matrix factorization (NMF) is a matrix decomposition method that decomposes a data matrix into two small-sized factor matrices, where all the three matrices
consist of nonnegative entries. The estimated two factor matrices facilitate easy interpretation of data matrix because of the sparseness under the nonnegative constraints.
Various NMF methods are based on an optimization problem that minimizes the distanceor divergence between the given data matrix and low rank approximation matrix, which
is a matrix product of the two factor matrices. This optimization problem is derived
from the maximum likelihood problem of the probabilistic model of NMF. Each entryof the data matrix is an independent and identically distributed random variable, whose
expected value is the corresponding entry of the low rank approximation matrix under
the given two factor matrices.
On the contrary, there are some Bayesian methods for NMF wherein the posterior of thetwo factor matrices is obtained under the appropriate prior of the two. [1] proposed theBayesian inference method with the composite model. In this method, each entry of thedata matrix is a sum of latent variables, as many as the bases of the two factor matrices.
[1] considers Poisson for the latent variables. Thus, the entry also follows Poisson becauseof the reproductive property. The full conditional of the two factor matrices can be derived
analytically using the composite model.
Furthermore, [1] presented the calculation method of the marginal likelihood using Chib’s
method [2]. The marginal likelihood is useful for the determination of the number of basesof NMF. However, it may depend on selecting the hyperparameters of the prior. In thisstudy, the influence of the prior to the model order selection is shown by computation
simulation on the advanced Bayesian NMF [3], which has not presented the marginal
likelihood calculation, and how to select the hyperparameter is studied.
Keywords: Nonnegative matrix factorization, model order selection, prior

References[1] A. T. Cemgil (2009). Bayesian inference for nonnegative matrix factorisation models.
Computational intelligence and neuroscience 2009.
[2] S. Chib (1995). Marginal likelihood from the Gibbs output. Journal of the american
statistical association, 90(432), 1313–1321.
[3] O. Gouvert, T. Oberlin, and C. Févotte (2018). Negative Binomial Matrix Factorization for Recommender Systems. arXiv preprint arXiv:1801.01708.

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CPS_11: Tuesday 13 (16:20-17:20) room RY106, page: 9

Causal eﬀect of inquiry teaching using generalized
propensity score in Japan
K. Hishiyamaa and K. Okadaaa Graduate School of Education, the University of Tokyo

In Japan, “proactive, interactive, and deep learning” have recently been promoted in
education, giving rise to inquiry teaching. Inquiry teaching is a teaching method thatis characterized by its emphasis on autonomous learning rather than one-way teaching.
It is believed to be eﬀective in educational achievement, especially in science classes. [1]investigated the causal eﬀect of inquiry teaching through a propensity score analysis using
data from the PISA 2006 report. They defined five levels of openness in inquiry teaching
and classified students based on their definitions. The results showed that the scienceachievement of students who experienced a moderate openness level of inquiry teaching
was the highest in Japan. However, the data they used were collected more than a decadeago; at present, the more recent PISA 2015 report is available to researchers. Hence, inthis study, we investigate the causal eﬀect of inquiry teaching in Japan by analyzing datafrom the PISA 2015 report by using a generalized propensity score analysis.
We analyzed data regarding 6,647 Japanese students, who were first classified as per thefive levels of openness in inquiry teaching experienced by them, using the definitions set
by [1]. First, we created an indicator treatment variable tij that takes the value of 1 if the
respondent i is treated in the j-th level of inquiry teaching, and 0 otherwise. Then, thegeneralized propensity score eij was estimated based on a multinomial logistic regressionin which the outcome is tij and the predictors are the 37 covariates based on [2]. Finally,
the causal eﬀect was estimatedbased on the inverse probability weighted marginal mean∑Nt y /eijij iof each level Ê(yj ) = ∑i=1
N

t /eiii=1 ij.

The results suggest that a moderate level of openness in inquiry teaching leads to thehighest achievement, while a more intensive level of inquiry teaching leads to adverse
eﬀects. The implications of these results on the design of educational programs are discussed.
Keywords: inquiry teaching, statistical causal inference, generalized propensity score

References[1] F. Jiang and W. F. McComas (2015). The eﬀects of inquiry teaching on student
science achievement and attitudes: Evidence from propensity score analysis of PISA
data. International Journal of Science Education, 37(3), 554–576.
[2] G. W. Imbens (2000). The role of the propensity score in estimating dose-response
functions. Biometrika, 87(3), 706–710.

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CPS_11: Tuesday 13 (16:20-17:20) room RY106, page: 9Computerized Adaptive Test that Incorporates Response Time for Personality Measurement
K. Fujitaa and K. Okadaaa The University of Tokyo

We propose a computerized adaptive testing (CAT, [1]) of the item response theory(IRT) model that incorporates response time for personality measurement. For IRTbased personality measurement, the distance-diﬃculty hypothesis, which claims that the
response time decreases as the distance between an item and respondent locations on alatent continuum increases, has been well-established. However, IRT models that formallyrepresent the distance-diﬃculty relationship have not been extended to CAT. This has
prevented researchers and practitioners from eﬀectively presenting items in a survey.
To solve this problem, we extended [2]’s model by treating several of its parameters as
respondent parameters that capture individual diﬀerences. In establishing the CAT of thismodel, two approaches were considered in selecting an item to present to a respondent.
One was to select an item having maximum item information, and the other was to selectan item having maximum information per time unit. The latter was quantified by dividing
the Fisher information of the item by its expected response time [3].
We conducted a comparative simulation in which item pool size and item parametervalues regarding response time were manipulated. The results revealed that the proposed
CAT approach resulted in higher estimation precision than did random item selection.
We also found that selecting the item that had the maximum information per time unit
performed better when items in the item pool contained a diverse range of response
time-related item parameter values or when the item pool size was suﬃciently large. We
also applied the proposed and existing methods to real item response and response time
data in which extraversion was measured. We concluded that the proposed CAT for IRT
model that incorporates response time successfully improves the estimation accuracy of
respondent traits and enhances survey eﬃciency.
Keywords: computerized adaptive test, response time, personality

References[1] Mulder, J. & van der Linden, W. J. (2009). Multidimensional adaptive testing with
optimal design criteria for item selection. Psychometrika, 74, 273–296.
[2] Ferrando, P. J. & Lorenzo-Seva, U. (2007). An item response theory model for incorporating response time data in binary personality items. Applied Psychological
Measurement, 31, 525–543.
[3] Fan, Z., Whang, C., & Chang, H. H. (2012). Utilizing response time distributions foritem selection in CAT. Journal of Educational and Behavioral Statistics, 37, 655–670.

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CPS_11: Tuesday 13 (16:20-17:20) room RY106, page: 9

Success/failure prediction using a large-scale online
testing system with its learning analytics
H. Hiroseaa Hiroshima Institute of Technology

Nowadays, it is crucial to identify students at risk of failing courses and/or droppingout as early as possible. By adopting a large-scale online testing system [1] such aslearning comprehension testing (LCT) for every class to measure student comprehensionof lectures, we can accumulate information for learning analytics. This paper is aimed
at producing eﬀective learning strategies for students at risk by utilizing the learning
analytics obtained from the online testing system.
To do this, we have proposed a newly developed method to identify students likely failin end-term examination by analyzing the similarity of the trends of estimated students’abilities obtained by item response theory. The nearest neighbor methodology related tothe method of NNRMLR ([2], e.g.) is adopted for analizing the similarity of learning skill
in the trends of estimated abilities. In determining the optimal threshold value for successor failure prediction, we have applied receiver operating characteristic curve and recall
precision curve in precisely investigating the accuracy of the proposed method. Using
data accumulated in 2017 ﬁscal year, we have constructed a model and its parameters
for a fundamental mathematics subject, resulting in higher prediction accuracy for 2018
ﬁscal year success/failure than that using only the item response theory result with a full
response matrix of M (n, m), where the number of students n is more than 1,000, and thenumber of items is almost 100.
Therefore, pedagogical implications focusing on the ﬁndings of this research are the following: 1) it is important to accumulate learning data such as LCT week by week in orderto support students at risk, 2) even if only the ﬁrst half of the ability trends are available,
we can predict the risk of failure at the end-term examination using the similarities ofability trends more accurately than using only the LCT abilities via the full response
matrix.
Keywords: success/failure prediction, large-scale online testing, learning analytics.

References[1] H. Hirose, Meticulous Learning Follow-up Systems for Undergraduate Students Using the Online Item Response Theory, 5th International Conference on Learning
Technologies and Learning Environments, 2016, pp.427-432.
[2] H. Hirose, NNRMLR2: An Improved Combined Method of Nearest Neighbor Regression and Multiple Linear Regression, 3rd IMS Asia Paciﬁc Rim Meetings, Paper:
220534, 2014.

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Invited Talk: Tuesday 13 (17:25-18:10) room RYB2, page: 9Multiscale bootstrap for selective inference with applications to model selection
Hidetoshi Shimodaira
Kyoto University / RIKEN AIPBootstrap resampling has been widely used for calculating confidence level of discretedecisions such as hierarchical clustering and variable selection. The bootstrap probabilityof a specified hypothesis is calculated as the frequency of observing the same hypothesis
in bootstrapped datasets. Although the bootstrap probability can be interpreted as a
Bayesian posterior probability, it is biased as a frequentist p-value, where the bias is understood geometrically in the parameter space as the “curvature” of the boundary surfaceof the hypothesis region (Efron and Tibshirani 1998). This bias is successfully adjusted
by the multiscale bootstrap (Shimodaira 2004) that utilizes the scaling-law of the bootstrap probability when changing the sample size of bootstrap datasets. However, there isanother bias caused by a kind of multiplicity of testing many hypotheses at the same. To
avoid this bias, a new approach called “selective inference” has been advocated, where theconditional probability is considered for selection of the hypothesis and applied to variableselection of Lasso (Taylor and Tibshirani 2015). In this talk, I present recent developmentof multiscale bootstrap for selective inference with applications to hierarchical clusteringand phylogenetic inference (Terada and Shimodaira 2017, Shimodaira and Terada 2019).
Keywords: Bootstrap resampling, Multiple testing, Selective inference.

References[1] Efron, B. and Tibshirani, R. (1998). The problem of regions.Annals of Statistics, 26,
1687–1718.
[2] Shimodaira, H. (2004). Approximately unbiased tests of regions using multistepmultiscale bootstrap resampling. The Annals of Statistics, 32, 2616–2641.
[3] Shimodaira, H. and Terada, Y. (2019). Selective inference for testing trees and edgesin phylogenetics. Front. Ecol. Evol., doi:10.3389/fevo.2019.00174.
[4] Taylor, J. and Tibshirani, R. (2015). Statistical learning and selective inference. Proceedings of the National Academy of Sciences of the United States of America, 112,
7629–7634.
[5] Terada, Y. and Shimodaira, H. (2017). Selective inference for the problem of regions
via multiscale bootstrap, arXiv preprint, arXiv:1711.00949.

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Keynote: Wednesday 14 (9:00-10:00) room RYB2, page: 10

Human vs computer: In Visualising Data, Who Wins?
Dianne Cook and Shuofan Zhang
Dept of Econometrics and Business Statistics, Monash University

Can computers relieve data analysts of the arduous task of graphically diagnosing models?
Computer vision has come a long way in recent years. The models produced can now
be used to automatically inspect the quality of items emerging along production lines,
identify objects in photos and even navigate an autonomous vehicle.
Despite the fact that visualisation plays a major role in data analysis, the use and interpretation of graphics by data scientists/statisticians is subjective. Analysts rely almost
entirely on their own judgement, years of experience and an implicit calculation of uncertainty when interpreting graphics. Considering data plots as a type of statistic encourages
towards an inferential approach to reading data plots. By formalising data visualisationin this way, we can explore the possibility of training a computer vision model to do thisvisual inference task.
In this talk, I will give an introduction to these ideas and then present the results of computer vision model for evaluating residual plots, used for diagnosing statistical model)
fits, comparing them to human evaluations of the same plots. Who do you think wins?
Keywords: Data science, Statistical inference, Statistical graphics.

References[1] Majumder, M., Hofmann, H. and Cook, D. (2013) Validation of Visual Statistical
Inference, Applied to Linear Models, Journal of the American Statistical Association, 108(503):942–956. Featured Article http://amstat.tandfonline.com/doi/
pdf/10.1080/01621459.2013.808157.
[2] Hofmann, H., Follett, L., Majumder, M. and Cook, D. (2012) Graphical Tests forPower Comparison of Competing Designs, IEEE Transactions on Visualization andComputer Graphics, 18(12):2441–2448, http://doi.ieeecomputersociety.org/
10.1109/TVCG.2012.230.
[3] Wickham, H., Cook, D., Hofmann, H. and Buja, A. (2010) Graphical Inference for
Infovis, IEEE Transactions on Visualization and Computer Graphics, 16(6):973–
979, http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.161. Best paper
award.

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Invited Talk: Wednesday 14 (10:00-10:45) room RYB2, page: 10

Copula-based semiparametric models for spatio-temporal
data
Y. Tanga , H. Wangb , Y. Sunc and A. Heringd
a East China Normal University, b George Washington University, c King Abdullah University of

Science and Technology, d Baylor University

The joint analysis of spatial and temporal processes poses computational challenges due tothe data’s high dimensionality. Furthermore, such data are commonly non-Gaussian. We
introduce a copula-based spatio-temporal model for analyzing spatio-temporal data andpropose a semiparametric estimator. The proposed algorithm is computationally simple,
since it models the marginal distribution and the spatio-temporal dependence separately.
Instead of assuming a parametric distribution, the proposed method models the marginal
distributions nonparametrically and thus offers more flexibility. The method also provides
a convenient way to construct both point and interval predictions at new times and newlocations, based on the estimated conditional quantiles. Through a simulation study andan analysis of wind speeds observed along the border between Oregon and Washington, weshow that our method produces more accurate point and interval predictions for skewed
data than those based on normality assumptions.
Keywords: Copula; Markov process; Spatio-temporal– 96–

ITS_3: Wednesday 14 (11:15-12:45) room RY101, page: 10

Robust singular spectrum analysis: Methodology andapplication
P. C. Rodriguesa
a Federal University of Bahia, Salvador, Brazil

Singular Spectrum Analysis (SSA) is a powerful and widely used non-parametric method
to analyze and forecast time series. Although SSA has proven to outperform traditionalparametric methods for model fit and model forecasting, one of the steps of the SSA algorithm is the singular value decomposition (SVD) of the trajectory matrix, which is very
sensitive to the presence of outliers because it uses the L2 norm optimization. Therefore,
the presence of outlying observations has a significant impact on the SSA reconstruction
and forecasts. The main aim of this paper is to introduce several robust alternatives to
SSA, where the SVD is replaced by the: (i) robust SVD based on the L1 norm; (ii) robust
SVD based on Huber function; and (iii) robust regularized SVD. Moreover, and aiming at
providing an exhaustive comparison, we also consider three robust methods for principal
component analysis to decompose the trajectory matrix. The SSA and the six robust SSA
alternatives are compared in terms of model fit and model forecasting via Monte Carlosimulations based on synthetic and real data, considering several contamination scenarios.
Joint work with Mohammad Kazemi, Rahim Mahmoudvand and Vanda Loureno.
Keywords: Singular spectrum analysis, Time series, Robust statistics.

References[1] M. Kazemi, and P.C. Rodrigues (2019). Robust singular spectrum analysis: Comparison between classic and robust approaches for model fit and forecasting (submitted)[2] P.C. Rodrigues, V.M.Loureno, and R. Mahmoudvand (2018). A robust approach to
singular spectrum analysis. Quality and Reliability Engineering International, 34,
1437–1447.

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ITS_3: Wednesday 14 (11:15-12:45) room RY101, page: 10

Call center arrivals: When to jointly forecast multiple
streams?
Haipeng Shen
University of Hong Kong, Hong Kong

We consider call centers that have multiple (potentially inter-dependent) demand arrival
streams. Workforce management of such labor intensive service systems starts with forecasting future arrival demand. We investigate the question of whether and when to jointly
forecast future arrivals of the multiple streams. We first develop a general statistical model
to simultaneously forecast multi-stream arrival rates. The model takes into account three
types of inter-stream dependence. We then show with analytical and simulation studies how the forecasting benefits of the multi-stream forecasting model vary by the type,direction, and strength of inter-stream dependence. In particular, we find that it is beneficial to simultaneously forecast multi-stream arrivals (instead of separately forecastingeach stream), when there exists inter-stream lag dependence among daily arrival rates.
Empirical studies, using two real call center datasets further demonstrate our findings,
and provide operational insights into how one chooses forecasting models for multi-stream
arrivals.
Keywords: time series forecasting, workforce management, uncertainty

References[1] Noah Gans, H. Shen, Yong-Pin Zhou, Nikolay Korolev, Alan McCord, Herbert Ristock (2015). Parametric Stochastic Programming Models for Call-Center Workforce
Scheduling. Manufacturing and Service Operations Management, 17, 571–588.

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ITS_6: Wednesday 14 (11:15-12:45) room RY102, page: 10

Prediction of cancer patients’ responses to drugs via
cell lines and machine learning
Grace S. Shieh
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

In the era of precision medicine, an important issue is to stratify cancer patients into
subgroups for precise treatment. Owing to patients’ molecular profiles may not be available, computational methods using omics data of cell lines and their drug responses frompublic domains may help increase the success rates of treatment.
Large-scale drug sensitivity screens in cell lines were used to identify clinically meaningful
gene-drug interactions. However, prediction of drug response of cancer patients remains
to be improved. Iorio and colleagues published valuable results and data (GDSC; Cell,
2016), which included the drug sensitivity scores and omics data of 1000+ cell lines treatedby 265 drugs. They fitted comprehensive logic models using combinations of sifted cancer
functional events (CFEs). However, they limited the number of CFEs to four at most.
To predict drug responses of lung cancer patients, we first fitted statistical models via several variable selection methods and training the penalty coefficients therein using omics
data in GDSC. Next, we compared the fitted models using external data sets, which included Cancer Cell Line Encyclopedia and an in vivo clinical trial data, consisting of gene
expression data of lung cancer patients and their overall survival. After the external validations, the algorithm developed may be useful for stratification of patients, in addition
to findings on variable selection methods suitable for drug response.
Keywords: Cancer, Omics data, Variable selection.

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ITS_6: Wednesday 14 (11:15-12:45) room RY102, page: 10

Predicting one-day-ahead wind power capacity factor
via functional inverse regression
Lu-Hung Chena and Ci-Ren Jiangba National Chung-Hsing University, b Academia Sinica

Inverse regression [2] is an appealing dimension reduction method for regression models
with multivariate covariates. Recently, it has been extended to the cases with functionalor longitudinal covariates, e.g., see [1]. However, the extensions simply focus on one single
functional or longitudinal covariate. Motivated by a real application, we extend functional
inverse regression to the cases with multiple functional covariates, whose domains could be
diﬀerent. The asymptotical properties of the proposed estimators are investigated. The
computational issues are taken care with data binning, the fast Fourier transformation
and random projections on a multi-core computation platform. In addition to simulation
studies, the proposed approach is applied to predict the wind power capacity factor of thenext day with the weather forecasts made today. Both demonstrate the good performanceof our method.
Keywords: Big Data, Functional Data Analysis, Inverse Regression.

References[1] Ci-Ren Jiang, Wei Yu, and Jane-Ling Wang. Inverse regression for longitudinal data.
Annals of Statistics, 42:563–591, 2014.
[2] Ker-Chau Li. Sliced inverse regression for dimension reduction. Journal of American
Statistical Association, 86(414):316–327, June 1991.

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ITS_6: Wednesday 14 (11:15-12:45) room RY102, page: 10

A dimension reduction method for cryo-em image processing
S. C. Chunga , S. H. Huanga , P. Y. Niua , S. Y. Huanga , W. H. Changb and I. P. Tuaa Institute of Statistical Science, b Institute of Chemistry

Academia Sinica, Taiwan

Within the past five years, many resolution breakthroughs through single particle cryoelectron microscopy (cryo-EM) analysis has transformed it into a mainstream technology
for high-resolution protein structure determination [1]. In contrast to X-ray crystallography, cryo-EM avoids crystallization in preparing particle samples and thus brings thehope to solve many important macro-protein structures. However, 3D structure determination from cryo-EM images is extremely challenging in computation because of low
signal to ratio. In this work, we propose a dimension reduction method called Two Stage
Dimensional Reduction (2SDR). 2SDR is to apply multi-linear principal component analysis (MPCA) [2] on the image set followed by PCA on its core score vectors. We showthat 2SDR can greatly reduce the computation cost in reducing parameters comparing
with Principle Component Analysis (PCA) while maintain similar or even better performance for cryo-EM image data sets. We further provide the asymptotic property of
2SDR. We demonstrate that 2SDR can realize effective de-noising for cryo-EM images on
two cryo-EM benchmark data sets are used to test our method: a 70S Ribosome set anda beta-galactosidase set.
Keywords: Image Analysis, Dimension Reduction, Visualization.

References[1] Editor (2016). Method of the year 2015. NATURE METHODS, 13(1), 1.
[2] Hung Hung, Pei-Shien Wu, I-Ping Tu and Su-Yun Huang (2012). On Multilinear
Principal Component Analysis of Order-two Tensors. Biometrika, 99, 569-583.

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CPS_12: Wednesday 14 (11:15-12:45) room RY105, page: 11

Cluster detection based on restricted hierarchical structure for large scale data
Y. Takemuraa , F. Ishiokaa and K. Kuriharaa
a Graduate school of environmental and life science, Okayama University

Information such as human activity and natural environment that describes with spatial
topological information is called spatial information, and it is abstracted and processedsuch that it can be used for statistical analysis is called spatial data. In recent years, spatial data can be easily collected by position information terminals such as GIS and GPS,and is widely used in a wide range of fields. When performing this statistical analysis ofspatial data, certain spatial events may be concentrated in a specific area. At this time,
it is said that “a cluster exists”. So far, many methods have been proposed to evaluate
the presence or absence of clusters. A spatial scan statistic [2] is widely used in the fieldof epidemiology and so on. However, it has been pointed out that Kulldorff’s proposed
circular scan method cannot detect non-circular clusters correctly because the clusters arescanned concentrically. For this method, in order to detect clusters of arbitrary shapes,
an echelon scan method [1] based on the hierarchical structure of spatial data obtainedby echelon analysis [3] has been proposed. Also, a spatial scan statistic based on theidea of maximizing likelihood have the problem of detecting large clusters with unrealistic
shapes. To solve this problem, Tango [4] proposed spatial scan statistic with a restricted
likelihood ratio. However, even with these methods, it is difficult to detect clusters withhigh likelihood of arbitrary shapes for large-scale spatial data with a large number ofregions. In this study, in order to analyze large-scale spatial data, we propose a new
detection method using a screening criterion based on the idea of spatial scan statisticwith a restricted likelihood ratio. Furthermore, we compare it with existing methods bynumerical experiments.
Keywords: Spatial scan statistic, Echelon analysis, Cluster detection.

References[1] F. Ishioka, K. Kurihara, H. Suito, Y. Horikawa, and Y. Ono (2007). Detection of
hotspots for three-dimensional spatial data and its application to environmental pollution data. Journal of Environmental Science for Sustainable Society, 1, 15–24.
[2] M. Kulldorff (1997). A spatial scan statistic. Communication in Statistics, Theory
and Methods, 26, 1481–1496.
[3] W. L. Myers, G. P. Patil, and K. Joly (1997). Echelon approach to areas of concern
in synoptic regional monitoring. Environmental and Ecological Statistics, 4, 131–152.
[4] T. Tango (2008). A spatial scan statistic with a restricted likelihood ratio. Japanese
Journal of Biometrics, 29(2), 75–95.

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CPS_12: Wednesday 14 (11:15-12:45) room RY105, page: 11

Hotspot detection for epidemiological data analysis using R Shiny
S. Kajinishia , F. Ishiokaa and K. Kuriharaa
a Graduate School of Environmental and Life Science, Okayama University

In recent years, spatial data have been collected and studied in various fields. In particular,
research is actively conducted in the field of spatial epidemiology. In this field, the risk ofdeath and disease is expressed using risk indicators such as SMR(Standardized Mortality
Ratio) or EBSMR(Empirical Bayes Estimate of SMR). Furthermore, by expressing therisk index on a map, data can be captured spatially, and the occurrence situation can bevisually grasped. In such analysis, areas with high value often appear to be concentrated
in a certain area. An area where high values are concentrated is called a hotspot cluster,and the hotspot needs to be determined by a statistical method.
The spatial scan statistic [3] detects hotspots that maximize the likelihood function byscanning the subset of regions. Several methods have been proposed for determining thisarea. The echelon scan method [1, 2] is a method of detecting hotspots using echelon
analysis [4] which can systematically and objectively find topological structures of spatial
data using height of data and adjacent information. The feature is that analysis time isshort because data is taken into a hotspot from the top of the data structure, and analysis
can be performed even on large-scale data. Although there are some software that canperform spatial hotspot detection, there is not yet any software using the echelon scanmethod. So we developed the software using Shiny, an R package. Shiny is a packagethat makes it easy development interactive web applications. One of the features of the
Shiny application is that it can change only the necessary parameters and can be easily
recalculated.
In this research, we introduce the risk index and spatial scan statistics methods, anddescribe the echelon scan method. Finally, we will introduce the software we developed.
Keywords: Hotspot, R Shiny, Echelon analysis.

References[1] K. Kurihara (2003). Detection of Hotspot based on Hierarchical Spatial Structure.
Bulletin of the Computational Statistics of Japan, 15(2), 171–183. (in Japanese)[2] F. Ishioka, J. Kawahara, M. Mizuta, S. Minato, and K. Kurihara (2019). Evaluationof hotspot cluster detection using spatial scan statistic based on exact counting.
Japanese Journal of Statistics and Data Science. (in press)[3] M. Kulldorﬀ (1997). A spatial scan statistics. Communications in Statistics, Theory
and Methods, 26, 1481–1496.
[4] W. L. Myers, G. P. Patil, and K. Joly (1997). Echelon approach to areas of concern
in synoptic regional monitoring. Environmental and Ecological Statistics, 4, 131–152.

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CPS_12: Wednesday 14 (11:15-12:45) room RY105, page: 11

Testing different spatial and temporal units in detecting and visualizing crime concentration
T. Shimadaa , T.Saitoha , and Y.Yamanea
a National Research Institute of Police Science of Japan

The presentation aims to find spatial and temporal units that can detect crime concentrations well in Japan. Because crimes concentrate in space and time[1], crime analysts
can tackle with crimes by detecting and visualizing such concentrations. When doing so,choosing space-time unit is essential because different social process works to produce
criminals and crime opportunities in different space-time scales [2] [3].
Data are official crime report of residential burglary in 2 prefectures in Japan between
2012-2016. Approximately 35000 crimes are nested within 1908 census-tracts and 107
municipals. We first calculate GiNi coefficients for census-tracts and municipals. We thenconducted the Exploratory Spatial Data Analysis where spatial displacements of residential burglary were found in Tokyo[4]. Finally, the crime concentrations were visualized indifferent types of map and space-time aggregation units.
The Gini revealed that crimes are more concentrated in census tracts than in municipals.
Only 2% and 8% of the census tracts in each prefecture contributed the half of the crime,
whereas 27% and 37% of the mununipal were needed to explain the half of the crimes. By
comparing 3 map types of Local Moran map, choropleth map, kernel density estimation
map, former two were found to describe high-crime risk well in census tracts resolution,
while KDE failed to describe crime concentration in mezzo and macro scale. Meanwhile,
temporal trends were examined in two temporal scales of year and month and seasonality
were also inspected in month and week. Conventional use of monthly aggregated crime
figure failed to detect crime spikes in a holiday week where inhabitants go for trips.
In conclution, crime analysts should focus on neighborhood and weekly-level dynamics
of crime in crime analysis and visualization to better understanding and preventing crime.
Keywords: Spatial-temporal analysis of crime, Crime concentration, Neighborhood.

References[1] Weisburd, D.L. (2015). The law of crime concentration and the criminology of place.
Criminology, 53, 133–157.
[2] Hipp, J.R., and Kim, Y. (2017). Measuring Crime Concentration Across Cities ofVarying Sizes: Complications Based on the Spatial and Temporal Scale Employed.
Journal of Quantitative Criminology, 33, 595-632.
[3] Steenbeek, W., and Weisburd, D.L. (2016). Where the Action is in Crime? An
Examination of Variability of Crime Across Different Spatial Units in The Hague,
2001-2009. Journal of Quantitative Criminology, 32, 449-469.
[4] Shimada,T. (2004). Spatial diffusion of residential burglary in Tokyo: using exploratory spatial data analysis. Behaviormetrika, 31, 169-181.

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CPS_12: Wednesday 14 (11:15-12:45) room RY105, page: 11

Stat on-demand for World Grid Square Statistics
A.-H. Satoa,b , N. Makitac , and S. Nishimurad
a Yokohama City University, b JST PRESTO, c Shiga University, d National Statistics Center

Japanese oﬃcial statistics has a long history of grid square statistics based on latitude
and longitude, which date back to 1965. Various types of grid square statistics and data
have been produced based on oﬃcial statistics such as population census and economic
census as well as data collected in economic and environmental purposes. The grid squarecoding system has been adopted as Japanese Industrial Standards (JIS X0410) since 1976.
Recently, authors have proposed a world grid square coding system [1] as an extension to
the Japanese grid square coding system. Further, we identified four types of data sources(point data, polygon data, satellite images, and other grid square statistics) to produce
world grid square statistics from geospatial data.
In this study, we propose a concept of statistics on-demand for World Grid Square statistics. Stat on-demand is based on the Generic Statistical Business Process Model (GSBPM) [2] developed by UNECE. GSBPM consists of eight phases: Specify Needs, Design,
Build, Collect, Process, Analyze, Disseminate, and Evaluate.
We prototype stat on-demand for world grid square statistics as a public web service [3]based on data collected from OpenStreetMap (OSM) service [4]. Processes from request
acceptance to the dissemination of the world grid square statistics are automaticallyconducted in the public service. We propose the following four steps to form the Stat ondemand in real-time: (i) users check out points of interests; (ii) the service automaticallyaccesses Web API of Web Feature Service (WFS) [4] to collect the data on points ofinterests; (iii) the system computes world grid square statistics; and (iv) the system
disseminates them. The system oﬀers simple analytic functions based on the data andsoftware migration model through the public web service. The world grid square coding
system has a granularity hierarchy. Third level world grid squares, about 1km squares
tiles when applied in middle latitudes, can cover the whole globe with about 200 million
tiles. Since the types of points of interest dealt in WFS vary enormously, and the total
number of the world grid squares is vast, it is hardly possible to produce world grid squarestatistics for all the possible types of interests beforehand. However, our proposed service
can produce and serves the world grid square statistics in response to the request of users.
Keywords: World Grid Square coding system, Web Feature Service (WFS), OpenStreetMap (OSM), Stat on-demand.

References[1] A.-H. Sato, S. Nishimura, T. Namiki, N. Makita, H. Tsubaki (2018). World Grid
Square Data Reference Framework and its Potential Applications, 2018 IEEE 42nd
Annual Computer Software and Applications Conference (COMPSAC), pp. 398–409.
[2] https://statswiki.unece.org/display/GSBPM/GSBPM+v5.0[3] MESHSTATS http://www.meshstats.net/meshstats/[4] OpenStreetMap https://openstreetmap.org/– 105–

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A tool for deep automatic classification and visual analytics of complex spatio-temporal data
Elena Gininaa , Denis Gračaninb , Michael Behama , Rainer Splechtnaa and Krešimir Matkovićaa VRVis Research Center, Vienna, Austria, b Virginia Tech, Blacksburg, Virginia, USA

In spite of recent advances in data analysis techniques, exploration of complex, unstructured spatio-temporal data can still be challenging. A simple application of individual
techniques can address only partly the overall analysis. We propose an interactive solution with human in the loop, combined with automatic classification of entities based on
deep learning.
Our approach is illustrated using a publicly available dataset, a collection of individuallabeled audio recordings of different bird species, taken at specific place and time overan extended time period, for which additional metadata is provided. Objects of interest
are automatic classification of bird songs and patterns of bird movements. To classify thesongs of the different bird species, we project them onto spectrograms, and use a custom
designed Convolutional Neural Network. Due to the amount and quality of the exemplarydata, the validation accuracy does not exceed 68%. However, it turns to be sufficient todemonstrate the potential of using deep learning for automatic classification of complex
data, and, in particular, of bird songs.
Furthermore, our interactive visualization design aims to: i) show spatial distribution ofaudio recordings; ii) enable comparison of different species; iii) enable temporal comparisons; iv) examine temporal changes for different species. We deploy a well establishedcoordinated multiple views technology and introduce three new views, the centroids scatter plot, the distance plot view, and the spectrogram view.
Only a combination of both, deep learning and visualization techniques, makes it possible
to efficiently explore the data and supports the creation of a mental model of birds’ moving
patterns. The proposed approach is used to develop a unified tool for exploration of bird
songs data, called Bird Song Explorer [1, 2].
Finally, we speculate that the tool can be generally useful for other domains and datasets,beyond the bird-song example described, enabling exploration of classified entities as they
move through space and time.
Keywords: Visual Analytics, Deep Learning, Bird Songs Analysis

References[1] K. Matkovič, D. Gračanin, M. Beham, R. Splechtna, M. Meyer, E. Ginina (2019),
Visual Analysis of Bird Moving Patterns, accepted for publication in Proceedings of
the Computer Graphics International (CGI) 2019, Calgary, Canada.
[2] E. Sprengel, M. Jaggi, Y. Kilcher, T. Hofmann (2016), Audio Based Bird Species
Identification using Deep Learning Techniques, Proceedings of the Conference andLabs of the Evaluation Forum (CLEF) 2016, Évora, Portugal, 547–559.

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CPS_13: Wednesday 14 (11:15-12:45) room RY106, page: 11

Machine learning classification of functional brain imaging for Parkinsons disease stage prediction
G. H. Huanga and C. H. Linaa National Chiao Tung University

In this study, we analyze a dataset containing functional brain imaging from 6 normal
healthy controls, and 196 Parkinsons disease (PD) patients that are divided into 5 stages
according to the severity of illness. The goal is to predict patients PD illness stages via
their functional brain images. Used approaches include multivariate statistical methods(linear discriminant analysis (LDA), support vector machine (SVM), decision tree (DT),multi-layer perceptron (MLP)), ensemble learning models (random forest (RF), adaptive
boosting (AdaBoost)), and deep convolutional neural network (CNN). For statistical andensemble models, principal component analysis (PCA) is performed to extract features,
synthetic minority over-sampling technique (SMOTE) [1] is used to deal with imbalanced
data problem, and the best combination of hyper-parameters is found by grid search.
For CNN modeling, we apply image augmentation technique to increase and balance data
sizes over different disease stages, and we adopt transfer learning idea to bring pre-trained
VGG16 [2] weights and architecture into model fitting.
It is found that LDA and RF are the analytic approaches with the highest prediction
accuracy rate for traditional and ensemble models, respectively. Overall, the deep CNN
model outperforms other approaches, which can capture significant features from imaging, have a better separation of normal controls and patients with PD, and reach higher
classification accuracy.
Keywords: Deep neural network, Machine learning, Supervised classification.

References[1] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer (2011). SMOTE:
synthetic minority over-sampling technique. arXiv preprint, arXiv:1106.1813.
[2] K. Simonyan, and A. Zisserman (2014). Very deep convolutional networks forlargescale image recognition. arXiv preprint, arXiv:1409.1556.

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CPS_13: Wednesday 14 (11:15-12:45) room RY106, page: 11

Toward Understanding Expressive Power of Graph Convolutional Neural Networks
Kenta Oonoa,b , Taiji Suzukia,ca Preferred Networks, Inc., b The University of Tokyo, c RIKEN AIP

Motivated by the success of Deep Learning (DL) in various fields such as image recognition, natural language processing, and speech recognition, several attempts have been
made to apply DL to non-Euclidean data, especially, graph-structured data like chemicalcompounds, social networks, and polygons. Graph Convolutional Neural Networks (Graph
CNNs) [2, 3] have emerged as a promising approach for analyzing such data. However,despite their practical popularity, theoretical researches of graph CNNs have been less
explored. One of the fundamental questions of theoretical analysis of DL models is thecharacterization of DL model expressive power, that is, to identify function classes that
DL models can (approximately) represent. For classical models such as Fully-connected
Neural Networks and Convolutional Neural Networks, we have theoretical and empiricaljustification for deep and non-linear architectures: DL models can enhance representation
power by stacking many layers and adding non-linear functions (activation functions) inbetween [1, 4, 5]. However, compared to such models, we know little about how deep andnon-linear architecture can affect the expressive power of graph CNNs. In this presentation, we talk about recent work for understanding the expressive power of graph CNNs,
including ours.
Keywords: Deep Learning, Graph CNN, Expressive Power.

References[1] M. Chen, J. Pennington, and S. Schoenholz. Dynamical isometry and a mean field
theory of RNNs: Gating enables signal propagation in recurrent neural networks. In
Proceedings of the 35th International Conference on Machine Learning, volume 80 ofProceedings of Machine Learning Research, pages 873–882. PMLR, 10–15 Jul 2018.
[2] T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional
networks. In International Conference on Learning Representations, 2017.
[3] F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini. The graphneural network model. IEEE Transactions on Neural Networks, 20(1):61–80, 2009.
[4] M. Telgarsky. benefits of depth in neural networks. In 29th Annual Conference on
Learning Theory, volume 49 of Proceedings of Machine Learning Research, pages 1517–
1539. PMLR, 23–26 Jun 2016.
[5] P. Zhou and J. Feng. Understanding generalization and optimization performance ofdeep CNNs. In Proceedings of the 35th International Conference on Machine Learning,volume 80 of Proceedings of Machine Learning Research, pages 5960–5969. PMLR,
2018.

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Generalized kernel quadrature for synthesizing neuralnetworks
S. Sonodaa
a RIKEN Center for Advancer Intelligence Project (AIP)

We propose a novel numerical integration method for training a shallow neural networkby using the ridgelet transform with a norm convergence guarantee. Given a training
dataset, the ridgelet transform can provide the parameters of the neural network thatattains the global optimum of the training problem (Sonoda et al., 2018). In other words,
we can obtain the global minimizer of the training problem by numerically computing theridgelet transform, instead of by numerically optimizing the so-called backpropagation
training problem. We employ the kernel quadrature (Chen et al., 2010; Bach et al., 2012)
for the basis of the numerical integration. Originally, the kernel quadrature has beendeveloped for the purpose of computing posterior means, where the measure is assumed
to be a probability measure, and the final product is a single number. On the other
hand, our problem is the computation of an integral transform, where the measure is
generally a signed measure, and the final product is a function. In this paper, we develop
a generalized kernel quadrature method for signed measures with a norm convergenceguarantee, and propose a natural choice of kernels. See (Sonoda, 2019) for more details.
Keywords: Neural networks, Kernel quadrature, Ridgelet transform.

References
Sho
Sonoda,Isao
Ishikawa,
Masahiro
Ikeda,
Kei
Hagihara,
Yoshihiro
Sawano,Takuo
Matsubara,andNoboruMurata.
The global optimum of shallow neural network is attained by ridgelet transform.
arXiv:1805.07517, 2018.
Yutian Chen, Max Welling, and Alex Smola. Super-Samples from Kernel Herding. In
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pages 109–
116, Catalina Island, CA, 2010.
Francis
Bach,
Simon
Lacoste-Julien,andGuillaume
Obozinski.
On the Equivalence between Herding and Conditional Gradient Algorithms.
In
Proceedings of the 29th International Conference on Machine Learning, pages
1355–1362, Edinburgh, Scotland, UK, 2012.
Sho Sonoda.
Unitary Kernel Quadrature for Training Parameter Distributions.
arXiv:1902.00648, 2019.

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ITS_4: Wednesday 14 (13:55-15:25) room RY101, page: 11

Visualizing dynamics of cooperative activities of neurons for neural coding studies
H. Shimazakia,ba Graduate School of Informatics, Kyoto University, b Honda Research Institute Japan

Neurons in the brain communicate each other using electrical pulses known as spikes.
Information of external world in the brain is therefore represented in spiking activity ofneuronal networks. However it is difficult to decipher how they code the information
based on direct inspection of the spike sequences due to their excessive variability, which
is not merely noise but a hallmark of cortical information processing realized by balanced
activity of excitatory and inhibitory neurons. Thus we need visualization techniques that
uncover statistical structure of the neural population activity.
Using binary variables to represent presence or absence of spikes in a small time window,
one can describe synchronous/asynchronous activity of multiple neurons as sequences ofbinary patterns. By fitting an undirected graphical model to such data, one can visualize
statistical network structure of the simultaneous activity of neurons. However this conventional model is limited because it assumes static neural activity (i.e., constant spike
rates and correlations), which could be realized only in in-vitro networks or neurons underanesthetized conditions. In-vivo neurons recorded from awake animals engaged in tasks ismore dynamic: The activity rates of neurons dynamically changes in response to stimulus
presentation, and strength of correlations between neurons may change. These statisticalfeatures are not only regulated by the stimulus but also an internal state of the brain.
To account for such dynamics, we introduce a state-space model of neural population activity, and visualize coordination of the neural activity using a time-varying graph [1, 2, 3].
In this talk, I will explain how this visualization technique reveals macroscopic propertiesof the network dynamics, and how it contributes to neural coding studies.
Keywords: Neuroscience, State-space model, Visualization.

References[1] Shimazaki H, Amari S, Brown EN, and Gruen S. (2012) State-space analysis of timevarying higher-order spike correlation for multiple neural spike train data. PLOS
Computational Biology, 8(3): e1002385.
[2] Donner C, Obermeyer K, Shimazaki H. (2017) Approximate inference for timevarying interactions and macroscopic dynamics of neural populations. PLOS Computational Biology, 13(1): e1005309.
[3] Gaudreault J and Shimazaki H. (2018) State-space analysis of an Ising model reveals
contributions of pairwise interactions to sparseness, fluctuation, and stimulus codingof monkey V1 neurons. Lecture Notes in Computer Science, 13(1): 11141, 641–665.

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ITS_4: Wednesday 14 (13:55-15:25) room RY101, page: 11

Principles of fMRI multivariate analysis and its applications in cognitive neuroscience
Kenji Ogawa
Department of Psychology, Hokkaido University

Functional MRI (fMRI) has been one of the most popular tools for neuroimaging in
cognitive neuroscience. fMRI has better spatial resolution compared with other noninvasive functional neuroimaging methods. It measures the whole brain with a spatial
resolution of typically 2-3 mm3 , which is called a voxel. Recently, multi-voxel patternanalysis (MVPA) has been gaining popularity for the fMRI studies, which enables theinvestigation of neural representations beyond brain activations in a conventional massunivariate analysis [1].
We first introduce the principles and merits of MVPA, which includes the pattern classification and representational similarity analysis (RSA). We then present some of ourapplications in cognitive neuroscience researches, including the study of the neural representations of observed actions in the human mirror-neuron system [2] as well as awake
reactivations in the motor-related cortical regions for visuomotor learning [3].
Keywords: fMRI, Multivoxel patten analysis, Cognitive neuroscience.

References[1] Haynes JD. (2015) A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. Neuron, 56(2), 257-270.
[2] Ogawa K. and Inui T. (2011) Neural representation of observed actions in the parietal
and premotor cortex. Neuroimage, 234(12), 728-735.
[3] Ogawa K., Yang H., Imai F., and Imamizu H. (2019) Awake reactivation in the primary sensorimotor cortex after visuomotor learning in humans. 26th annual meetingof the Cognitive Neuroscience Society.

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ITS_4: Wednesday 14 (13:55-15:25) room RY101, page: 11

Visualizing mental template of attractive facial imagesof humans and Deep Neural Network
T. Naitoa , R. Imaib and H. Satoa,ba Grad. Sch. Med., Osaka Univ., b Grad. Sch. Front. BIosci., Osaka Univ.

Facial attractiveness may influence many human social interactions (e.g. promotion, voting, and mate choice)[1]. Therefore, there are many studies that investigated the facialcharacteristics influencing attractiveness judgments of faces (e.g. self-similarity, symmetry, and size of eyes)[2]. A recent study reported that the Deep Convolutional Neural
Network (DCNN) obtained hierarchical learned the concept of facial attractiveness of human observers [3]. However, it is still unclear whether the mechanisms of the DCNN under
the facial attractiveness judgment was similar with that of humans or not. In this study,
we show that DCNN learned the taste of individuals with high accuracy as previous studies reported. Then, we reconstructed visual mental image of facial attractiveness of bothparticipants and the DCNN using reverse correlation technique. For all participants andcorresponding DCNN, the visual image of attractive face was successfully reconstructed.
We confirmed that the mental template of DCNN was significantly correlated with thatof the participant who provided the facial attractiveness scores for learning. Our results
suggested that the DCNN that learned ones taste for facial attractiveness reconstructedsimilar judgement mechanisms with humans in it.
.
Keywords: Psychological reverse correlation, facial attractiveness, mental template of
attractive face.

References[1] A. Todorov, P. Mende-Siedlecki, and R. Dotsch2 (2013). Social judgments from faces.
Current Opinion in Neurobiology, 23, 373–380.
[2] A. Little, B. Jones, and L. Debuine (2018). Facial attractiveness : evolutionary based
research. Philosophical Transactions of the Royal Society B: Biological Sciences, 366,
1638–59.
[3] Y. Y. Fan, S. Liu, B. Li, Z. Guo, A. Samal, and J.Wan (1998). Label DistributionBased Facial Attractiveness Computation by Deep Residual Learning. 20(8), 2196–
2208.

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ITS_7: Wednesday 14 (13:55-15:25) room RY102, page: 12

An attention algorithm for solving large scale structured l0-norm penalized estimation problems
Tso-Jung Yena and Yu-Min Yenba Institute of Statistical Science, Academia Sinica, Taiwan, b Department of International Busi-ness, National Chengchi University, Taiwan

Technology advances have enabled researchers to collect large amounts of data with lotsof covariates. Because of the high volume (large n) and high variety (large p) properties,
model estimation with such kind of big data has posed great challenges for statisticians.
In this paper we focus on the algorithmic aspect of these challenges. We propose a numerical procedure for solving large scale regression estimation problems involving a structured
l0 -norm penalty function. This numerical procedure blends the ideas of randomization,
blockwise coordinate descent algorithms, and a closed form representation of the proximal operator of the structured l0 -norm penalty function. In particular, it adopts an“attention” mechanism that exploits the residual errors to build a sampling distribution
for picking up regression coefficients for updates. Simulation study shows the proposed
numerical procedure is competitive to the benchmark algorithm for sparse estimation interms of runtime and statistical accuracy when both the sample size and the number ofcovariates become large.
Keywords: Blockwise coordinate descent algorithms, nonconvex optimization, randomized algorithms.
Yen, T.-J. and Yen, Y.-M. (2018). An attention algorithm for solving large scale structured l0 -norm penalized estimation problems (submitted).

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ITS_7: Wednesday 14 (13:55-15:25) room RY102, page: 12

Statistical Methods and Active learning
Yuan-chin Ivan Chang
Institute of Statistical Science, Academia Sinica, Taiwan

Active learning is a subfield of machine learning. The word ”active” is referred to subject
selection and therefore related to the ideas of sequential methods in statistical literature.
In addition, if the information from analyzing the previous samples in hands in used for
subject selection, then it is related to stochastic regression. The methods of statisticalexperimental design for parametric models give us guidances for subject selection, when
there is a preselected model considered. For nonparametric model, it is hard to follow
those “letter” criterions, we then borrow the thinkings of the robust statistical methods
instead of design theories. In this talk, we will discuss how to integrate these conventional
statistical methods/ideas for classification problems under active learning scenarios.
Keywords: Active learning, Classification, Sequential Design.

References[1] Bo-Shiang Ke, An Jen Chiang and Yuan-chin Ivan Chang (2017). Influence Analysis for the Area Under the Receiver Operating Characteristic Curve, Journal ofBiopharmaceutical Statistics, 28(4), 722 - 734.
[2] Hsiang-Ling Hsu, Yuan-chin Ivan Chang and Ray-Bing Chen (2018). Greedy Active
Learning Algorithm for Logistic Regression Models, Computational Statistics andData Analysis, 129, 119 – 134.
[3] Yuan-chin Ivan Chang and Ray-Bing Chen (2019). Active learning with simultaneous
subject and variable selections, Neurocomputing, 329, 495 – 505.
[4] Zhanfeng Wang, Yumi Kwon, Yuan-chin Ivan Chang (2019). Active learning for binary classification with variable selection, arXiv:1901.10079.

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ITS_7: Wednesday 14 (13:55-15:25) room RY102, page: 12

Robust linear discriminant analysis based on γ-divergence
T.-L. Chena , C.-H. Wua and W.-S. Hoba Academia Sinica, b National Taiwan University

Linear discriminant analysis (LDA), a traditional method in linear classification, is widelyused in pattern recognition and dimension reduction. In many practical cases, some data
points are mislabeled, which may badly affect the LDA results. In this talk, we will
first introduce γ-divergence which is a more robust dispersion measure than the widelyused Kullback-Leibler divergence. Based on minimum γ-divergence, we proposed a morerobust LDA type method. Instead of sample mean and sample covariance derived byminimum K-L divergence, weighted sample mean and weighted sample covariance fromγ-divergence can successfully reduce the effects of incorrect labels. We will analyze therobustness of our proposed method via its influence function. In the end, we will demonstrate the strength of our proposed method by simulation studies and applications on facedata sets.
Keywords: Robustness, discriminant analysis, influence function.

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CPS_14: Wednesday 14 (13:55-15:25) room RY105, page: 12

Shrinkage estimator for the precision matrix and its
optimization in closed form
I. Nagai
Department of International Liberal Studies, School of International Liberal Studies, Chukyo
University

We consider the n × p multivariate data Y which made from n-sample and each sample
has p-dimensional data. Here, we assume n ≥ 2 and Cov[vec(Y)] = Σ ⊗ In , where Σ is an
unknown p × p matrix with rank(Σ) = p, In is a n × n identity matrix, vec(·) means vec
operator, and ⊗ means the Kronecker product (the definitions of them are in e.g., [2]).
Usually, we estimate Σ as S = Y′ (In − 1n 1′n /n)Y/(n − 1) where 1n is a n-dimensional
vector whose all elements are 1. Letting di be the ith eigenvalue of S, we note di ≥ 0(i = 1, . . . , p). In almost ordinary estimation methods for analyzing the multivariate dataY, we often need S−1 which is called precision matrix.
However, S−1 does not exist when rank(S) < p since some di become 0. By the way, when
there is high correlated variables between the columns of Y, some corresponding di tend
to become very small positive values. Then, S−1 becomes unstable. In these cases, many
ordinary methods can not use or become untrusted method for analyzing Y. Thus, wemust use the alternative matrix to S−1 .
One of the alternative matrices to S−1 is proposed by [3] as λ(S + αIp )−1 where λ andα are positive parameters. This proposed matrix stabler than S−1 since this matrix is
adding α (> 0) to di which means we can get stable (S + αIn )−1 if some di = 0 or several
di s are very small values. On the other hand, we can regard λ as a scale parameter.
Using this matrix has two problems. The first problem is that we need some simultaneous
optimization algorithm for α and λ. Thus, we have to use iterative methods for optimizingthem. The other problem is that we adjust d1 , . . . , dp by using only one parameter α.
In order to avoid these problems, we propose a new alternative matrix to S−1 or λ(S +αIp )−1 , based on the method in [1]. Letting Q be the orthogonal matrix which diagonalizes
S, θ1 , . . . , θp be the nonnegative parameters, and Θ be diagonal matrix whose diagonal
elements are θ1 , . . . , θp , we propose the new matrix as following form;
S−1 (λ, θ1 , . . . , θp ) = λ(S + QΘQ′ )−1 .
In this estimator, we can see that each θi coincides with each di . Thus, this new propose
estimator becomes more flexible than λ(S + αIp )−1 . Moreover, we will show that the
optimized θ1 , . . . , θp based on some Loss functions in e.g., [3] are derived in closed form.
Keywords: Generalized ridge regression, Shrinkage estimator.

References[1] A. E. Hoerl and R. W. Kennard (1970). Ridge regression: applications to nonorthogonal problems. Technometrics, 12, 69–82.
[2] J. R. Schott (2017) Matrix Analysis for Statistics (Third Edition).
[3] C. Wang, G. Pan, T. Tong, and L. Zhu (2015). Shrinkage estimation of large dimensional precision matrix using random matrix theory. Statistica Sinica, 25, 993–1008.

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CPS_14: Wednesday 14 (13:55-15:25) room RY105, page: 12

The de-biased group Lasso estimationfor varying coefficient models
Toshio Hondaa
a Hitotsubashi University

There has been a lot of attention on the de-biased or de-sparsified Lasso since it was
proposed in 2014 ([1], [2], [3]). The Lasso is very useful in variable selection and obtaining initial estimators for other methods in high-dimensional settings. However, it
is well-known that the Lasso produces biased estimators. Therefore several authors simultaneously proposed the de-biased Lasso to fix this drawback and carry out statisticalinferences based on the de-biased Lasso estimators. The de-biased Lasso procedures needdesirable estimators of high-dimensional precision matrices for bias correction. Thus theresearch is almost limited to linear regression models with some restrictive assumptions,generalized linear models with stringent assumptions and the like. To our knowledge,
there are a few papers on linear regression models with group structure, but no result
on structured nonparametric regression models such as varying coefficient models. In
this paper, we apply the de-biased group Lasso to varying coefficient models and closely
examine the theoretical properties and the effects of approximation errors involved in
nonparametric regression. Some numerical results are also presented.
Keywords: High-dimensional data, group Lasso, bias correction.

References[1] Javanmard, A. and Montanari, A.(2014). Confidence intervals and hypothesis testingfor high-dimensional regression. J. Machine Learning Research, 15, 2869–2909.
[2] van de Geer, S., Bühlmann, P., Ritov, Y. and Dezeure, R.(2014). On asymptotically optimal confidence regions and tests for high-dimensional models. Annals of
Statistics, 42, 1166–1202.
[3] Zhang, C.-H. and Zhang, S. S. (2014). Confidence intervals for low dimensional parameters in high dimensional linear models. J. Royal Statist. Soc. Ser. B, 76, 217–242.

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CPS_14: Wednesday 14 (13:55-15:25) room RY105, page: 12Sparse and Functional Principal Components Analysis
Michael Weylandta and Genevera Allena,b,c
aDepartment of Statistics, Rice University (Houston, TX USA)bDepartments of CS and ECE, Rice University (Houston, TX USA)
cNeurological Research Institute, Baylor College of Medicine (Houston, TX USA)

Regularized variants of Principal Components Analysis, especially Sparse PCA and Functional PCA, are among the most useful tools for the analysis of complex high-dimensional
data. Many examples of massive data have both sparse and functional (smooth) aspects
and may benefit from a regularization scheme that can capture both forms of structure. For
example, in neuro-imaging data, the brain’s response to a stimulus may be restricted to adiscrete region of activation (spatial sparsity), while exhibiting a smooth response withinthat region. We propose a unified approach to regularized PCA which can induce both
sparsity and smoothness in both the row and column principal components.
Our framework generalizes much of the previous literature, with sparse,[2] functional,[3]two-way sparse,[4] and two-way functional PCA [5] all being special cases of our approach.
Our method permits flexible combinations of sparsity and smoothness that lead to improvements in feature selection and signal recovery, as well as more interpretable PCA
factors. Finally, we demonstrate the efficacy of our method on simulated data and an
neuroimaging example on EEG data.
Keywords: Regularized PCA, Multivariate Analysis, Dimension Reduction
References:[1] G.I. Allen and M. Weylandt. “Sparse and Functional Principal Component Analysis.”
To appear in DSW 2019: Proceedings of the 2019 IEEE Data Science
Workshop. ArXiv 1309.2895.
[2] H. Shen and J. Z. Huang. “Sparse principal component analysis via regularized low
rank matrix approximation.” Journal of Multivariate Analysis 99(6), pp.1015-1034.
2008. doi:10.1016/j.jmva.2007.06.007.
[3] B. W. Silverman. “Smoothed Functional Principal Components Analysis by Choiceof Norm.” Annals of Statistics 24(1), pp.1-24. 1996. doi:10.1214/aos/1033066196.
[4] G.I. Allen, L. Grosenick, and J. Taylor. “A Generalized Least-Square Matrix Decomposition.” Journal of the American Statistical Association 109(505), pp.145-159.
2014. doi:10.1080/01621459.2013.852978.
[5] J.Z. Huang, H. Shen, and A. Buja. “The Analysis of Two-Way Functional Data Using Two-Way Regularized Singular-Value Decompositions.” Journal of the American Statistical Association 104(488), pp.1609-1620. 2009.
doi:10.1198/jasa.2009.tm08024.

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CPS_15: Wednesday 14 (13:55-15:25) room RY106, page: 13

Interpreting multiple regression via an ellipse inscribedin a square extensible to any finite dimensionality
Toshiyuki Shimono
Digital Garage, Inc., Tokyo, Japan

Despite widely used in statistics, multiple regression exhibits several problems, such as
the interpretation and the instability of its numerical results, and multicollinearity as well.
The novel geometrical representations in this study may aid in solving them, furthermore,
can be used to visually illustrate the numerical results for the cases of low dimensionality.
Let Xi be the explanatory variable vector for each i = 1, 2, . . . , p and Y be the response
variable vector, all of which are of n-dimensional if n observations are performed. Define ρi , ρij as the Pearson product-moment correlation coeﬃcients between Xi and Y , Xi
and Xj , respectively, for i, j ∈ {1, 2, . . . , p}. The correlation matrix RX×X := (ρij ) on
X1 , . . . , Xp is a positive semidefinite p×p matrix, and assume it has full rank. A col⊤.
umn vector RX×Y is defined to consist of (ρi )i=1,...,p and a row vector RY×X := RX×Y
Upon performing the multiple regression the multiple correlation coeﬃcient RX ⊢Y equals−1(RY×X RX×X
RX×Y )1/2 [1, pp.73]. The partial regression coeﬃcient a⊙i corresponding to Xi−1equals the i-th element of RX×X RX×Y for i = 1, . . . , p [1, pp.68, 78] if all of Xi and Y arestandardized. The partial correlation between Xi and Y fixing the other variables X⋏i is
−1
2
2
1/2
(RXi ×Y −RXi×X⋏i RX[1, pp.77], where RXi ×Y = ρi .
⋏×X⋏ RX⋏×Y )/{(1−RX⋏ ⊢X )(1−RX⋏ ⊢Y )}
i
i
i
i
i
i
By applying linear algebra and elementary geometry, we will obtain some remarkablegeometric properties. Consider a p-dimensional space Rp with orthogonal axes labeled
x1 , x2 , . . . , xp . Let S ⊂ Rp be the boundary of a square (p = 2) or a (hyper) cube (p ≥ 3)
surrounded by lines or (hyper) planes with xi = ±1 (i = 1, . . . , p). Separate the matrix
RX×X into p pieces of p-dimensional vectors as RX×X = (⃗
ρ1 | . . . |⃗ρp ). Let E ⊂ Rp be an
ellipse or a (hyper) ellipsoid inscribed in S. The condition S ∩ E = {±⃗ρ1 , ±⃗
ρ2 , . . . , ±⃗
ρp }
p
⊤ −1
holds iﬀ E = {x ∈ R | x RX×X x = 1} holds which is assumed henceforth, i.e., E is“drawn” by RX×X . Let a point P ∈ Rp have the coordinates (ρ1 , . . . , ρp ) = RX×Y . (Forp = 2, 3, see the figures above.) Theorem 1 (Multiple correlation coeﬃcient): LetP ′ be the intersection of E and the half-line from the origin O toward P . RX ⊢Y equals
the ratio of the two Euclidean distances |OP |/|OP ′ |. Theorem 2 (Partial regression
coeﬃcient): For each i = 1, . . . , p let a linear function fi : Rp → R map any pointfrom S ∩ E\{±⃗ρi } into 0 and ρ⃗i into 1. a⊙i equals fi (P ) in the case when Xi and Y
are all standardized. Theorem 3 (Partial correlation coeﬃcient): Let two points
Pi− , Pi+ ∈ S form the line segment Pi− Pi+ going through P with parallel orientation to
the xi -axis. Let an aﬃne map gi : R → Rp satisfy gi (1) = Pi+ and gi (−1) = Pi− . The
partial correlation coeﬃcient fixing all variables other than Xi and Y equals gi−1 (P ).
Keywords: Multiple regression, Geometric interpretation, Inscribed ellipse.

References[1] Kei Takeuchi, Haruo Yanai (1972). Tahenryou Kaiseki No Kiso. Toyo Keizai Inc.

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CPS_15: Wednesday 14 (13:55-15:25) room RY106, page: 13

Nonlinear quantile mixed-effect models based on basis
expansions
Yuta Tanabe and Yuko Araki
Department of Informatics, Guraduate School of Integrated Science and Technology, Shizuoka
University

Quantile regression [1] is the method for estimating conditional quantile of response variable. In this framework, nonlinear quantile mixed model [2] was proposed to analyze
the transition of the quantiles of a certain reaction in longitudinal data with nonlinear
structure. However, in this model, one needs to set the parametric nonlinear equation inadvance and this is often difficult.
To solve this problem, we proposed nonlinear quantile mixed-effects models based on
basis expansions. This proposed model can generalize and represent nonlinear structuresof quantiles by linear combinations of basis functions. Further, by defining basis functions
as both fixed-effect terms and random-effect terms, it is possible to express the model inthe form of a mixed-effects model and one can use it for longitudinal data analysis. In
addition, since the number of basis functions is determined for each quantile of the analysis
target according to the information criterion, it is possible to flexibly adapt to the cases
where different quantiles have different non-linear structures.
Parameter estimation is performed by maximum likelihood estimation, based on asymmetric Laplace distribution, and the estimates are finally obtained numerically by MCEM(Monte
Carlo Expectation - Maximization)algorithm.
The performance of the proposed method is evaluated by numerical experiments andshowed its usefulness in terms of fitting nonlinear transition of the quantiles.
Finally, the proposed model is applied to longitudinal data of cortisol levels in newborns.
Keywords: Quantile regression, Basis expansion, Mixed-effects models.

References[1] Koenker and Bassett(1978). Regression Quantiles. Econometrica, 46(1), 33–50.
[2] Geraci(2017). Nonlinear quantile mixed models. arXiv Preprint arXiv:1712.09981.
[3] Araki, Konishi, Kawano and Matsui(2009). Functional reggression modeling via regularized Gaussian basis expansions. Advances in Data Analysis and Classification.,61(4), 811–833.

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CPS_15: Wednesday 14 (13:55-15:25) room RY106, page: 13

Properties of the Ramp Function as
an Activation Function in Deep Neural Networks
T. Yanagimotoa , and K. Ohkusaba Institute of Statistical Mathematics, b Kyushu University

The ramp function is defined for constants satisfying au > al and cu > cl as,f (x; al , au , cl , cu ) = Max{cl , Min{cl + [(cu − cl )/(au − al )](x − al ), cu },which has been employed as a regression function [1] and also as a loss function in themachine learning. As a limit, it contains ReLU function in deep neural networks (DNN),
and equivalently the hockey stick regression function [2]. We first note that it satisfies
the three attractive properties; 1) the natural spline function of degree 1 with the two
knots al and au , 2) the lightest tails among the symmetric unimodal functions and 3) Our
easy understanding of the model. An important point concerns the two bents containedin the function. In fact, such bents are not realistic to describe accurately the true model,
and a smooth function are considered to be more realistic. Recall that special attentions
have been paid on the bents in the regression analysis. On the other hand, the simple
form of the function makes our understanding easy. In addition, the process of our actual
observation in a brain is expected to be achieved in a simple manner.
We conducted the comparison study to examine the performance of DNN with the rampfunction as an activation function.
Keywords: Easy understanding, Hockey-stick regression, Performance comparison

References[1] M. Mudelsee (2000). Ramp function regression: a tool for quantifying climate transitions. Computers & Geosciences, 26, 293-307.
[2] T. Yanagimoto and E. Yamamoto (1979). Estimation of safe doses: Critical reviewof the hockey stick regression method. Env. Health Persp., 32, 193-199.

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CPS_15: Wednesday 14 (13:55-15:25) room RY106, page: 13

Robust penalized empirical likelihood estimation method
for a linear regression model
O. Arslana and S. Ozdemirb
a Ankara University, b Afyon Kocatepe University

Two challenging problems of the regression analysis are the parameter estimation andvariable selection. Concerning the parameter estimation, the least squares regression estimation method is usually used to estimate the parameters. However, since least squares(LS) regression estimators are highly sensitive to the outliers robust parameter estimation
methods are often used to obtain regression estimators that are resistant to the outliersin the data. Alternative to these methods nonparametric methods such as empirical likelihood are also used to estimate the regression parameters if we do hesitate to impose any
distributional assumptions on the error term. Concerning the variable selection, recent
trend is to combine variable selection with parameter estimation. One of the first combinations is the LASSO regression method. After its original introduction, many researchers
have been proposed several different modifications of LASSO method. These modifications include using robust objective function to gain robustness to possible outliers in data
and/or using different penalty functions to improve oracle properties of resulting estimators. Among these penalty functions the bridge penalty function is the popular one as
the generalization of the LASSO penalty function and ridge penalty function. Alternative
to the existing literature, the aim of this study is to combine robust empirical likelihood
method and the bridge penalty function to simultaneously carry on robust parameter
estimation and variable selection in a linear regression model for which any distributional
assumptions on its error term are not plausible. We study asymptotic properties of the
proposed estimator and explore the finite sample behavior with a simulation study and areal data example.
Keywords: Empirical Likelihood, Robust regression, Variable Selection.

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Invited Talk: Wednesday 14 (15:55-16:40) room RYB2, page: 13

Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
Genevera Allen
Rice University, USA

Convex clustering is a promising new approach to the classical problem of clustering,combining strong performance in empirical studies with rigorous theoretical foundations.
Despite these advantages, convex clustering has not been widely adopted, due to its
computationally intensive nature and its lack of compelling visualizations. To addressthese impediments, we introduce Algorithmic Regularization, an innovative technique forobtaining high-quality estimates of regularization paths using an iterative one-step approximation scheme. We justify our approach with a novel theoretical result, guaranteeing global convergence of the approximate path to the exact solution under easily-checked
non-data-dependent assumptions. The application of algorithmic regularization to convex
clustering yields the Convex Clustering via Algorithmic Regularization Paths (CARP) algorithm for computing the clustering solution path. On example data sets from genomics
and text analysis, CARP delivers over a 100-fold speedup over existing methods, whileattaining a finer approximation grid than standard methods. Furthermore, CARP enables improved visualization of clustering solutions: the fine solution grid returned by
CARP can be used to construct a convex clustering-based dendrogram, as well as forming
the basis of a dynamic path-wise visualization based on modern web technologies. Our
methods are implemented in the open-source R package clustRviz. This is joint work withMichael Weylandt and John Nagorski.

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ITS_5: Wednesday 14 (16:45-18:15) room RY101, page: 13

Characterizing Variability of Brain Connectivity with
Constrained Principal Component Analysis
J. Hirayamaa,b , A. Hyvärinenc,d,a , V. Kiviniemie , M. Kawanabeb,a and O. Yamashitab,aa RIKEN Center for Advanced Intelligence Project,b Advanced Telecommunications Research Institute International (ATR),c INRIA-Saclay, d University of Helsinki, e Oulu University Hospital

Characterizing the variability of the brain’s functional network organization is of fundamental importance in basic neuroscience as well as clinical researches. Functional connectivity is usually measured as pairwise covariances or correlations of neural signals,
typically for 5-10 minutes of resting-state functional magnetic resonance imaging (fMRI)
data. The inter-subject variability of this connectivity matrix, or its associated graph,has been linked to individual diﬀerences in cognitive function and neurological healthor disease. In order to discover characteristic patterns from connectivity matrices collected from many subjects (and/or time windows), principal component analysis (PCA)
has been successfully applied to analyzing the variability of connectivity matrices [1].
However, performing PCA on high-dimensional connectivity matrices yields complicated“eigenconnectivity” patterns, for which systematic interpretation is a challenging issue.
In order to overcome this issue, we first developed a module-constrained PCA techniquecalled orthogonal connectivity factorization (OCF) [2], and extended this approach to a
new method called modular connectivity factorization (MCF) [3]. MCF simultaneously
finds network modules and analyzes the variabilities of both inter-module connectivity
and intra-module connectivity in an interpretable manner. We developed an optimization
algorithm to solve the constrained PCA problem and validate our method in simulation
studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The
results show that the proposed MCF method successfully reveals the underlying modular
eigenconnectivity patterns in more general situations and is a promising alternative toexisting methods.
Keywords: Brain Connectivity, Variability, Constrained PCA.

References[1] N. Leonardi et al. (2013). Principal components of functional connectivity: A new
approach to study dynamic brain connectivity during rest. NeuroImage, 83, 937–950.
[2] A. Hyvärinen et al. (2016). Orthogonal connectivity factorization: interpretable decomposition of variability in correlation matrices. Neural Computation, 28(3), 445–
484.
[3] J. Hirayama et al. (2016). Characterizing variability of modular brain connectivity
with constrained principal component analysis. PLoS ONE, 11(12), e0168180.

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ITS_5: Wednesday 14 (16:45-18:15) room RY101, page: 13Compression based bound for non-compressed deepneural network models and their data adaptivity
Taiji Suzukia,ba The University of Tokyo, b Center for Advanced Intelligence Project, RIKEN

One of biggest issues in deep learning theory is its generalization ability despite the hugemodel size. The classical learning theory suggests that overparameterized models cause
overfitting. However, practically used large deep models avoid overfitting, which is not well
explained by the classical approaches. To resolve this issue, several attempts have beenmade. Among them, the compression based bound is one of the promising approaches[1, 4]. However, the compression based bound can be applied only to a compressed
network, and it is not applicable to the non-compressed original network. In this paper,
we give a unified frame-work that can convert compression based bounds to those fornon-compressed original networks. The bound gives even better rate than the one forthe compressed network by improving the bias term. By establishing the unified framework, we can obtain a data dependent generalization error bound which gives a tighter
evaluation than the data independent ones. Especially, we derive a tight compression
based bound for networks with near low rank properties on both the weight matrices andthe covariance matrices in internal layers.
Related to the compression bound, we will discuss the adaptivity of deep learning, that is,
deep learning can extract appropriate bases in an adaptive way to each target function.
Due to this property, deep learning can achieve superior performance than kernel methods
especially on models with non-convexity [3, 2]. This point shares several similarities tothe sparse learning methods such as L0 regularization and low rank matrix estimation. In
particular, low rank property gives better generalization error which connects to the firsthalf of the topic in this talk.
Keywords: Deep learning, Generalization error analysis, Function approximation

References[1] S. Arora, R. Ge, B. Neyshabur, and Y. Zhang. Stronger generalization bounds for deep
nets via a compression approach. ICML2018, volume 80 of Proceedings of Machine
Learning Research, pp. 254–263, 2018.
[2] S. Hayakawa and T. Suzuki. On the minimax optimality and superiority of deep neuralnetwork learning over sparse parameter spaces. arXiv:1905.09195, May 2019.
[3] T. Suzuki. Adaptivity of deep reLU network for learning in besov and mixed smoothbesov spaces: optimal rate and curse of dimensionality. In International Conference
on Learning Representations (ICLR), 2019.
[4] T. Suzuki, H. Abe, T. Murata, S. Horiuchi, K. Ito, T. Wachi, S. Hirai, M. Yukishima,
and T. Nishimura. Spectral-Pruning: Compressing deep neural network via spectral
analysis. arXiv:1808.08558, Aug 2018.

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Machine Learning Approach to Topological Data Analysis
Kenji Fukumizu
The Institute of Statistical Mathematics

Topological data analysis (TDA) is a recent methodology for extracting topological andgeometrical features from data of complex geometric structures. Persistent homology, a
new mathematical notion proposed by Edelsbrunner (2002), provides a multiscale descriptor for the topology of data, and has been recently applied to a variety of data analysis. In
this work I will introduce a machine learning framework of TDA by combining persistencehomology and kernel methods. A method of kernel embedding of persistence diagrams
to obtain their vector representation is presented, which enables one to apply any kernel
methods in topological data analysis. As a special kernel to persistence diagrams, thepersistence weighted Gaussian kernel is proposed and some theoretical properties are discussed [1]. The methods are applied to change point detection and time series analysis in
the field of material sciences and biochemistry.
Keywords: Persistence diagram, kernel method, topological data analysis, change point
detection)

References[1] Genki Kusano, Kenji Fukumizu, and Yasuaki Hiraoka (2018) Kernel Method for
Persistence Diagrams via Kernel Embedding and Weight Factor. Journal of Machine
Learning Research, 18(189):1-41.

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ITS_8: Wednesday 14 (16:45-18:15) room RY102, page: 14

Designing Experiments with Unstructured Treatments
for General Network Structures
Frederick Kin Hing Phoa
Institute of Statistical Science, Academia Sinica, Taiwan

Experiments on connected units are commonly conducted in many scientic elds. An experimental unit in these applications may connect with some others, and the treatment
applied to a unit has an eect, called a network eect, towards the responses resulted in
the neighboring units. Designing such experiments was rarely discussed in the literature.
Parker, Gilmour, and Schormans (2017) initiated a study of As-optimal designs on connected experimental units with unstructured treatments, assuming that the network eects
are unknown constants. This work investigates in a similar design problem but the network eects are assumed to be random eects, which lead to a property that the responsesof two units are correlated if some neighbors of one unit and those of the other receive thesame treatment. Alphabetical optimality criteria are considered for selecting good designs
with high eciency of estimating the treatment eects and/or high accuracy of predicting the
network eects. We provide theoretical conditions for designs to be optimal and illustrate
our theory with some numerical examples. This is a joint work with Dr. Ming-Chung
Chang of National Central University, Taiwan and Ms. Jin-Wen Huang, National Tsing
Hua University, Taiwan.
Keywords: Network Eect, Social Network, Mixed-Eect Model, Alphabetical Optimality– 127–

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Reliability and Validity of the Public Opinion Poll inJapan
Yuejun Zheng
a Faculty of Culture and Information Science, Doshisha University, JapanThe opinion poll based the statistical thinking was proposed by Gallup in the UnitedStates in 1930s, and has been introduced to Japan by the Allied Powers General Headquarters(GHQ) after World War II. Since the public opinion has become into a means to
the democratic government in the modern Japanese society, how to secure the reliability
and validity of an opinion poll has been considered as an important issue in the past
decades. During more than half a century, many significant changes happened in thesampling technique and the survey mode.
This paper focuses on verifying the feature and transition of the sampling technique andthe survey mode in Japanese opinion poll, and also discusses the influences of questionnairestyle, completion rate of interview, and post weighted tally. The results of data analysis
have revealed that it is necessary to reconstruct an RDD frame covering both fixed andcell phone numbers, and the improvement of completion rate is also an important issue
to guarantee the data quality.
Keywords: Stratified multi-stage sampling, Random digit dialing (RDD), Completion
rate of interview, Post weighted tally

References[1] Zheng Y. (2007): Developing An Area Sample Based on Street Maps for Social
Survey without Frames –A case study of consciousness survey conducted in Tokyo–,
Proceedings of the Institute of Statistical Mathematics. 55(2): 311–326.
[2] Zheng Y. (2017): Empirical Analysis on Influences of the Survey Mode on Data
Quality. Proceedings of the 45th Annual Meeting of the Behaviormetric Society. 45:
138–139.

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Multiplicity adjustment with Markov property in temporal and spatial epidemiology
Hisayuki Hara
Doshisha University

In this talk, we discuss a computaion of conditional expectation in multi-nomial distribution. The problem is motivated from the evaluation of the multiplicity-adjusted p-valueof scan statistics in spatial epidemiology. We propose a recursive summation/integration
technique using the Markov property in statistics. We extract the Markov property defined by chordal graphs in spatial and temporal structure. The proposed algorithm is
explained by information propagation on a clique tree.
This methodology works for a class of distributions including the Poisson distribution(that is, the conditional dis- tribution is the multinomial). As an illustration of theapproach, we present the real data analyses to detect temporal and spatial clustering.
This is a joint work with Satoshi Kuriki at Institute of Statistical Mathematics andKunihiko Takahashi at Nagoya University[1].
Keywords: chordal graph, clique tree, spatial epidemiology.

References[1] Kuriki, S., Takahashi, K. and Hara, H. (2018). Multiplicity A selection of best hypothesis. Japanese Journal of Statistics and Data Science, 1(1), 191–213.

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Exploratory Financial Big Data Analysis andReproducible Research
M. Jimichia , D. Miyamotob , C. Sakaa and S. Nagataa
a Kwansei Gakuin University, b University of Tokyo

In this work, we preprocess a financial big data set of world-wide companies that is
extracted from the database systems Osiris of Bureau van Dijk KK. It contains financial
information on over 90,000 listed companies. We load it to R (say, data wrangling [9]) byusing Apache Spark (e.g. [6]) and R packages SparkR. We also treat data visualization(e.g. [8]) and statistical modeling (e.g. [2]) based on exploratory data analysis [7] with
R. The dobule-log model with the skew-t error distribution (e.g. [1]) explains sales by
employees and total assets (see [4]). It will give one solution to predict reasonable sales,
given the human resources and asset size of all listed companies in the world. Note thatall processes of this work are reproducible (e.g. [3]) by using Unix command make andSweave (see [5]).
Keywords: Big financial data analysis, Visualization, Reproducible research.

References[1] A. Azzalini with the collaboration of A. Capitanio (2014). The Skew-Normal and
Related Families. Cambridge University Press, Institute of Mathematical Statistics
Monographs.
[2] Chambers, J. M. and Hastie, T. J. ed. (1991) Statistical Models in S. Chapman andHall/CRC.
[3] C. Gandrud (2015). Reproducible Research with R and RStudio, Second Edition, CRC
Press.
[4] M. Jimichi, D. Miyamoto, C. Saka, and S. Nagata (2018). Visualizationand Statistical Modeling of Financial Big Data: Log-Linear Modeling with
Skew Error. Japanese Journal of Statistics and Data Science, 1 (2), 347–371,
https://doi.org/10.1007/s42081-018-0019-1[5] F. Leisch (2002). Sweave: Dynamic generation of statistical reports using literate
data analysis. In Wolfgang Härdle and Bernd Rönz, editors, In Compstat 2002 Proceedings in Computational Statistics, 575–580. Physica Verlag, Heidelberg.
[6] S. Ryza, U. Laserson, S. Owen, and J. Wills (2016) Advanced Analytics with Spark,
O’REILLY.
[7] J. W. Tukey (1977). Exploratory Data Analysis, Addison-Wesley Publishing Co.
[8] A. Unwin (2015). Graphical Data Analysis with R. Chapman and Hall/CRC.
[9] H. Wickham and G. Grolemund (2016) R for Data Science, O’Reilly.

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Visualization of Corporate Tax Avoidance and Value
Added Distribution: Exploratory Analysis of Financial Big Data
C. Sakaa and M. Jimichiaa School of Business Administration, Kwansei Gakuin University

The purpose of this study is to explore the evidence of (1) the probability of firms’ tax
avoidance and the downward convergence trend of national statutory tax rates and firms
eﬀective tax rates, and (2) value added distribution to stakeholders (creditors, employees, governments and investors). This research employs exploratory data analysis using
interactive data manipulation and visualization tools, namely, R with SparkR, dplyr, ggplot2, and googleVis (GeoChart and Motion Chart) packages. This analysis is based on
the world-scale accounting data of all listed firms (over 90,000 firms) from 148 countries
spanning 30 years. The originality of this study are (1) A novel methodology is adopted
through the visualization of world-scale accounting data, which can facilitate a new perspective, revealing unexpected patterns and trends in otherwise hidden information, and(2) this study also highlights the importance of global consideration of firms’ tax avoidance, tax rate competition, and the changing trend of value added distribution using
objective evidence.
Keywords: Tax avoidance, Value added distribution, Exploratory data analysis

References[1] M. Jimichi, D. Miyamoto, C. Saka, and S. Nagata (2018). Visualization and Statistical Modeling of Financial Big Data: Double-log Modeling with Skew-symmetric
Error Distributions, Japanese Journal of Statistics and Data Science, 1 (2), 347–371,
https://doi.org/10.1007/s42081-018-0019-1.
[2] T. Oshika, and C. Saka (2017). Sustainability KPIs for Integrated Reporting, Social
Responsibility Journal, 13 (3), 625–642.
[3] C. Saka, and M. Jimichi (2017). Evidence of Inequality from Accounting Data Visualisation, Taiwan Accounting Review, 13 (2), 193–234.
[4] C. Saka, T. Oshika, and M. Jimichi (2018). Visualization of Tax Avoidance and Tax
Rate Convergence: Exploratory Analysis of World-scale Accounting Data, Meditari
Accountancy Research, forthcoming (accepted).

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On identification of agonistic interaction: Hepatitis B
and C interaction on hepatocellular carcinoma
Sheng-Hsuan Lina , Yen-Tsung Huangb , and Hwai-I Yangca Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwanb Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
c Genomics Research Center, Academia Sinica, Taipei, Taiwan

Agonistic interaction is one of the most important types of mechanistic interaction, which
is difficult to be distinguished from synergistic interaction by empirical data. In this study,
we propose four approaches that suffice to identify and estimate the agonistic interaction:(1) to make a strong assumption that synergy does not exist; (2) to exploit informationfrom a third factor by assuming that this factor is a necessary component for the background condition of synergistic interaction but is not involved in other mechanisms; (3) toconsider a third factor necessary for the background condition of agonistic interaction but
not involved in other mechanisms; and (4) similar to (3) but to allow flexibility that thethird factor may have a main effect on the outcome and/or a synergistic effect with thetwo risk factors of interest. We applied the proposed methods to quantify the agonism
of Hepatitis B and C viruses (HBV and HCV) infections on liver cancer using a Taiwanese cohort study (n = 23 820; HBV carrier n = 4149 (17.44%), HCV carrier n = 1313
(5.52%)). The result demonstrated that agonistic interaction is more dominant comparedwith synergistic interaction, which explains the findings that the dual infected patients
do not have a significantly higher risk of liver cancer than those with single infection. By
exploiting an additional risk factor that satisfies certain assumptions, these approaches
potentially fill the gap between mechanistic and causal interactions, contributing the comprehensive understanding of causal mechanisms.
Keywords: Agonistic interaction, Causal inference, Sufficient component cause model.

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Mixture Hidden Markov models to detect engagement
dynamics of mobile based Health program participants
S.S.M.Silva a , D. Meyera and M.W. Jayawardanaa
a Statistics, Data science and Epidemiology Department, Swinburne University of Technology,

Australia

World wide obesity has doubled since 1980 according to the World Health Organisation [1].
Due to the simultaneous improvement in sensor technology, mHealth interventions relating
to physical activity are gaining increasing popularity. However, these mHealth programs
provide only limited information regarding overall statistics and visualisation of tracked
performance data. Most of these programs consider only cross sectional data in order togroup participants depending on engagement levels. However, to provide a truly dynamic
personalisd environment what is needed are methods for identifying different engagement
trajectories. The Virgin Pulse Global Challenge is a mhealth program which runs with theaim of improving the awareness of employees regarding their physical activity and wellbeing [3]. In this current study, multichannel engagement trajectories for the main module
will be considered. The engagement trajectory for each participant will be comprised of
two parallel channels, namely the number of weekly logins and step entries.
In the literature there are many ways for clustering multichannel sequences using Hidden
Markov Models (HMM). In the current study the authors have used Mixture Hidden
Markov Models (MHMM) [2]. This model identifies the most probable cluster for eachparticipant depending on their engagement dynamics. Using random starting values 31
different models are simulated 100 times, varying the number of clusters from two to four,and allowing two to four hidden states for each cluster. As the number of clusters andthe number of hidden states for each cluster increases, the Bayesian Information Criterion(BIC) decreases rapidly until an elbow is reached. From the models near this elbow thebest model is selected on the basis of interpretability and simplicity. The final model
includes three clusters with two, four and four hidden states respectively. The clusters
and hidden states are named and transition probabilities between the states are visualised.
These transition probabilities will be used to create a personalised environment for eachparticpant, incorporating actionable suggestions and effective interventions in order toensure optimum benefit from the program.
Keywords: Mixture Hidden Markov Models, Engagement, Personalisation.

References[1] World health organization, obesity and overweight fact sheet 311, October 2017 04
November 2017 https://www.who.int/mediacentre/factsheets/fs311/en/.
[2] Satu Helske and Jouni Helske. Mixture hidden Markov models for sequence data: The
seqHMM package in R. Journal of Statistical Software, 88(3):1–32, 2019.
[3] Virgin Pulse. Global challenge | employee health & wellness program, February 2019https://www.virginpulse.com/global-challenge/.

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Sparse-Group Independent Component Analysis with
Application to Yield Curves Prediction
Ying Chena , Linlin Niub , Ray-Bing Chenc and Qiang Hea
a National University of Singapore, b Xiamen University, c National Cheng Kung University

Extracting and identifying essential factors have become increasingly important with therapid growth of big data in modern research. We propose a Sparse-Group Independent
Component Analysis (SG-ICA) method to extract independent factors from high dimensional multivariate data. The method provides a unified and flexible framework that automatically identifies the number of factors and simultaneously estimates a sparse loading
matrix, enables us to discover important features and offers improved interpretability ofthe estimators. We establish the consistency and asymptotic normality of the loading
matrix estimator, demonstrate its finite sample performance with simulation studies, andillustrate its application using the daily US Overnight Index Swap rates from Oct 2011
to Mar 2015 with 15 maturities ranging from 1 week to 30 years. With higher efficiencyof extracting factors, The forecasting performance of the SG-ICA is remarkably better
than the popular parametric DNS model in an era of quantitative easing with short-term
interest rate being close to zero.
Keywords: Regularized dimension reduction, Yield curve prediction, Statistically independent factor– 134–

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Bayesian Modelling Weekly Dengue Hemorrhagic Fever
CountsCathy W.S. Chen and K. Khamthong
Department of Statistics, Feng Chia University, Taiwan

This study focuses on modelling weekly dengue hemorrhagic fever (DHF) case counts innortheastern Thailand through two meteorological variables, cumulative rainfall and average maximum temperature. We propose two nonlinear integer-valued GARCH models(Markov switching and threshold specification) with a negative binomial distribution, as
they take into account the stylized features of DHF counts, which contain overdispersion,consecutive zeros, lagged dependence, nonlinear dynamics, and asymmetric eﬀects of meteorological covariates. The parameter estimation and model selection perform under aBayesian framework through a Markov chain Monte Carlo scheme with model selection
on a Bayes factor and deviance information criteria. A simulation study illustrates theestimation performance of the proposed methods, revealing findings in which the Markov
switching integer-valued GARCH model can describe weekly DHF case counts well andmaking it the best model among all competing ones. We also provide one-step-ahead
forecasting based on the prediction interval that oﬀers a useful early warning signal ofoutbreak detection.
Keywords: Yes factor, Consecutive zeros, Deviance information criteria, Markov chain
Monte Carlo scheme, Markov switching, Overdispersion; prediction intervals.

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A fast MM algorithm for Clusterpath
Patrick J.F. Groenena , Yoshikazu Teradab , Mariko Takagishib , and Tom van den Berga ,a Erasmus University Rotterdam, b Osaka University

Convex clustering is an elegant clustering approach proposed by Hocking et al. (2011) thatrephrases k-means clustering into a convex optimization problem with a grouped-Lasso
like penalty to induce equal cluster means. By changing the penalty strength parameter,a path is obtained that joins increasingly more clusters means. In this way, generally ahierarchy of clusters is obtained. The advantage of Clusterpath is that for each penalty
strength the resulting minimization problem is convex in the means and hence a global
minimum can be obtained. A disadvantage is that it cannot be guaranteed that it results
in a hierarchical tree of clusters. A second disadvantage is that the algorithms proposedso far are quite slow. In this paper, we propose three innovations that each can be used
independently. First, we propose to make use of MM algorithms that can decrease running
time of the algorithm by a factor 3 to more than 10, depending on the options chosen.
Second, to avoid bias of the cluster means towards the overall mean for larger penalty
strengths, we suggest to use the SCAD shrinkage penalty (Fan, & Li, 2001). Third, ahierarchy of partitions can be explicitly imposed that comes with the additional bonus ofseriously reducing the computational complexity. We present an illustrative example andsome numerical results.
Keywords: Hierarchical clustering, Penalty, SCAD.

References[1] Hocking, T. D., Joulin, A., Bach, F., & Vert, J. P. (2011, June). Clusterpath an algorithm for clustering using convex fusion penalties. In 28th international conferenceon machine learning (p. 1).
[2] Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood andits oracle properties. Journal of the American statistical Association, 96(456), 1348–
1360.

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Asymptotic distribution of sample correlation coeﬃcient under elliptical population
S. Nakagawaa , H. Hashiguchib and Y. Onoca Okayama University of Science, b Tokyo University of Science, c Yokohama City University

We discuss here an asymptotic distribution of the sample correlation coeﬃcient r undera nonnormal population, especially an elliptical population.
There are many works on the distribution of r, when the population is a bivariate normal.
For example, Hotelling [2] showed that the exact probability density function of r is given
by a Gauss hypergeometric function. Fisher’s z transformation z(r) = 1/2 log(1+r)/(1−r)
is commonly used as a normalizing transformation of r. Niki and Konishi [5] gave thehigher order asymptotic expansion of the distribution of z(r).
When the population is nonnormal, Nakagawa and Niki [3] gave the first fourth asymptotic
cumulants of order O(n−1 ), where n is a sample of size. Hayakawa [1] showed z(r) is also
a normalizing transformation under an elliptical population.
In this paper, we give the first sixth asymptotic cumulants of z(r) under an elliptical
population, where the order is O(n−2 ). Obtaining the cululants, we use here symmetric polynomial calculus which is implemented in Mathematica. Moreover, we give the
Edgeworth expansion and the Cornish–Fisher inverse expansion of z(r). The results areextended version of Nakagawa et al. [4].
Keywords: Edgeworth expansion, Symmetric polynomial calculus

References[1] Hayakawa, T. (1987), Normalizing and variance stabilizing transformations of multivariate statistics under an elliptical population, Annals of the Institute of Statistical
Mathematics, 39(2), 299–306.
[2] Hotelling, H. (1953), New light on the correlation coeﬃcient and its transforms,
Journal of the Royal Statistical Society. Series B (Methodological), 15(2), 193–232.
[3] Nakagawa, S. and Niki, N. (1992), Distribution of the sample correlation coeﬃcientfor nonnormal populations, Journal of the Japanese Society of Computational Statistics, 5 (1), 1–19.
[4] Nakagawa, S. and Niki, N. and Hashiguchi, H. (1998), Computer algebra application
to the distribution of sample correlation coeﬃcient, Mathematics and computers insimulation, 45 (1-2), 23–32.
[5] Niki, N. and Konishi, S. (1984). Higher order asymptotic expansions for the distribution of the sample correlation coeﬃcient, Communications in Statistics-Simulation
and Computation, 13 (2), 169–182.

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Inference on high-dimensional mean vectors underan alternative hypothesis
H. Watanabea , M. Hyodob and T. Seoca Oita University of Nursing and Health Sciences, b Osaka Prefecture University, c Tokyo Univer-sity of Science

For traditional multivariate data analysis, Hotelling’s T 2 test can be used. However, inmany modern applications, the number of features p exceeds the sample size n, thus
making the straightforward use of T 2 statistics impossible because the sample covariancematrix is singular. Several recent papers have addressed this issue. For example, Bai
and Saranadasa [2], Chen and Qin [4], Cai, Liu, and Xia [3], Aoshima and Yata [1]. To
test whether two populations have the same mean vector in a high-dimensional setting,anunbiased estimator of the squared Euclidean distance between the mean vectors has beenpreviously derived, and the asymptotic normality of this estimator has been proved underlocal assumptions about the mean vectors. These results are extended without assumptions about the mean vectors. In addition, asymptotic normality is established in a classof general statistics which includes important statistics under general moment conditions
that cover both the previous moment condition and elliptical distributional assumption.
These asymptotic results are applied to the construction of intervals for diﬀerences between mean vectors of two-groups. The finite-sample and dimension performance of the
proposed methods is also studied via Monte Carlo simulations. The methodology is illustrated using microarray data.
Keywords: High-dimensional data analysis, Asymptotic theory, Confidence interval.

References[1] M. Aoshima, K. Yata (2019). Two-sample tests for high-dimension, strongly spiked
eigenvalue models. arXiv:1602.02491.
[2] Z. Bai, H. Saranadasa (1996). Eﬀect of high dimension: by an example of a two
sample problem. Statist. Sinica, 6, 311–329.
[3] T. T. Cai, W. Liu, Y. Xia (2014). Two-sample test of high dimensional means under
dependence. J. R. Statist. Soc. B, 76, 349–372.
[4] S. X. Chen, Y. L. Qin (2010). A two-sample test for high-dimensional data withapplications to gene-set testing. Ann. Statist., 38, 808–835.

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A model selection criterion for missing data with MAR
mechanism but NMAR for its subsets
K. Hayashia and K. Takaiba Keio University, b Kansai University

It is important to select the best model among candidate models in statistical analysis.
This study focuses on a model selection criterion for missing at random (MAR) datasets.
The problem in using the MAR data is that the same mechanism does not necessarily holdfor its subsets . This fact implies that the consistent maximum likelihood (ML) estimatorof the interesting parameter cannot be obtained. Although various model selection criteria
have been developed, they are not applicable because they assume that the missing datamechanism for all possible subsets of data are also MAR. Thus, we need to construct a
procedure to estimate parameters consistently and a model selection criterion with theirestimators under more realistic situation: subsets are possibly NMAR mechanism. In thisstudy, we established the following: (i) a framework to obtain a consistent estimator of
the parameter of interest and its asymptotic distribution, (ii) a model selection criterion
for the subset of data which may be NMAR. Numerical experiments will be given to
demonstrate how well our method works under some practical conditions.
Keywords: Missing data, Missing mechanism, Model selection. (Use at most 3 keywords)

References[1] J. E. Cavanaugh and R. H. Shumway (1998). An Akaike information criterion formodel selection in the presence of incomplete data. Journal of Statistical Planningand Inference, 67(1), 45–65.
[2] K. Takai and Y. Kano (2013). Asymptotic inference with incomplete data. Communications in Statistics - Theory and Methods, 42(17), 3174–3190.

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Data Science as Mode 2 Science: Yokohama City University
K. Koizumi, Y. Ono, M. Taguri and J. Wang
School of Data Science, Yokohama City University, Japan

Abstract. Yokohama City University (YCU) created the undergraduate program on data sciencein April 2018. YCU will also start both master’s and doctoral degree programs on data science inApril 2020. In this paper we share some of our thoughts on data science underlying the researchand education at YCU. Some of these thoughts are general, so they may be relevant to research andeducation on data science on the whole.
Data Science as Mode 2 Science
Data science may be viewed from the statistical, computational and human perspectives. Each of theseperspectives is important, however, the very organic combination of these perspectives is essential todata science（Blei and Smyth (2017), Hernan, Hsu and Healy (2018)). Data Science belongs to thecategory of science, if we view it from the perspective mode 2 science (Baber, et al. (1995)). Unlike
traditional sciences, such as probability or statistics, data science, is by essence transdisciplinary,having its own context of applications, being socially responsible for its obtained results, with novel
quality control criterions to ensure the quality of results in additional the traditional peer reviews.
Organizational diversity is also essential to eﬀectively carry out data science programs.
YCU’s Practical Data Science Courses
Basic skills of data science include data-driven thinking, statistics, machine learning, programmingskills, data visualization and communication. Among these skills, the data-driven thinking may be
the most important one. One great characteristic of YCU’s data science programs is the carefully
designed Practical Data Science Courses. At the undergraduate level, we have the compulsory PBL(problem-based learning) at the junior level under the careful guidance of each supervisor and theassociated parters in business or government. For the master’s degree program, the PBL expands toPractical Data Science Courses, which lasts from the first year to the first half of the second year.
D-Step and WiDS Tokyo@Yokohama City University
Among the many activities at YCU, we choose to mention the Yokohama Data Science Educational Program (D-STEP, https://d-step.yokohama/), and WiDS Tokyo@Yokohama City University (https://wids-tokyo.jp/). D-STEP is a one-year certification program aiming to training 200
graduate level data scientists and 800 data experts over the next 4 years through collaboration fromthe industry, government and academia as well. WiDS Tokyo@Yokohama City University, with great
support from the industry as well, was held with tremendous success in March 2019 in Tokyo. It
inspired many data scientists nationwide, men and women alike.
Keywords: Data Science, Education, Science.

References[1] Z. Baber, M. Gibbons, C. Limoges, H. Nowotny, S. Schwartzman, P. Scott and M. Trow (1995).
The New Production of Knowledge: The Dynamics of Science and Research in Contemporary
Societies. Contemporary Sociology 24(6): 751.
[2] B. Healy,J. Hsu, and M.A. Hernán (2018) . Data science is science’s second chance to get causal
inference right: A classification of data science tasks.https://arxiv.org/abs/1804.10846.
[3] S. Padhraic, and D.M. Blei (2017). Science and data science. Proceedings of the National
Academy of Sciences114 (33), 8689-8692.

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CPS_18: Thursday 15 (9:30-10:30) room RY105, page: 15

A strategy of data science education related sustainable development goals
S. Izumia
a Shiga University

Data science is a way to extract value from big data. The technical foundation of data
science is data engineering (informatics) and data analysis (statistics). Shiga University
established a faculty of data science in April 2017 and graduate school of Data Science inApril 2019 in order to foster data scientists who have scientific skills and a human mind([1]). In this talk, we explain a strategy of data science education related sustainable
development goals at Shiga University([2]).
First, we summarize the outline of Shiga University model([3], [4]). Second, we overview
United Nations’ sustainable development goals (SDGs). Here we focus on Terms 4 and11([5]). Term 4 is to ensure inclusive and equitable quality education and promote lifelong
learning opportunities for all. Term 11 is to make cities and human settlements inclusive,
safe, resilient and sustainable. Third, we demonstrate interactive teaching methods anddeveloped teaching materials for data science education related to SDGs. Furthermore,
we will introduce some examples of collaboration with domestic organizations ([6]). We
hope that many universities will strengthen education in the data science field with reference to the Shiga University model.
Keywords: Big data, Society 5.0, Blended learning.

References[1] H. Maruyama, N. Kamiya, T. Higuchi, A. Takemura.2015. Developing data analytics skills in Japan: status and challenge. Journal of Japan Industrial Management
Association, 65, 334–9.
[2] S. Izumi and M. Hatayama. (2018). Overview of an educational model of elementary
statistics for the data science program of Shiga University. In Proceedings of the TenthInternational Conference on Teaching Statistics , 1A, 1–6.
[3] A. Takemura. (2018). Introduction to Data Science. Iwanami Shoten, Tokyo (inJapanese).
[4] A. Takemura, S. Izumi, K. Saito, T. Himeno, H. Matsui, H. Date.2018). Shiga University Model of Data Science Education. Proceedings of the Institute of Statistical
Mathematics, 66(1), 63–78 (in Japanese).
[5] United nations. (2017). Sustainable
https://sustainabledevelopment.un.org/.

Development

Knowledge

Platform.

[6] S. Izumi, M. Hatayama, K. Matsumiya. (2019). Data science education rooted in theregion. Proceedings of Statistical Education Practice Research, 21–6 (in Japanese).

– 141–

CPS_18: Thursday 15 (9:30-10:30) room RY105, page: 15

Philosophical foundations of the big data analysis anddata driven science
Y. Takeuchia
a Osaka University

This study tries to examine philosophical foundations of the big data analysis and data
driven science. It has been quite limited to study philosophical foundations of statisticalinference and statistical data analysis. For the case of the exploratory data analysis(EDA), which is proposed by John W. Tukey in 1970s, some researchers have pointed outthat the thought of the EDA is close to abduction by C. S. Pierce.
For the age of big data, in 2007 Jim Gray said that the age of the fourth paradigmof science, i.e. the exploratory science, is coming. This paradigm is thought as new
movement of empiricism. Kichen [1] and Fricke [2] examined the big data analysis anddata driven science from epistemological view. However, several questions on those still
remain. In this study I deal with two questions; (1) the link between specification of
conditional pattern and the general law, (2) disregarding the context of data.
To solve these two questions, I focus on recent realism movements in philosophy. One is
speculative realism proposed by Q. Meillassoux [3]. He criticizes the correlationism whichis common in modern science and try to disconnect the link mentioned above. Another is
new realism proposed by M. Gabriel [4]. Gabriel argue the world does not exist whereas
many context based fields exist.
To stand these realism movements, we find that big data analysis and the data driven
science might be diﬀerent from traditional statistics and science.

Keywords: Abduction, Speculative Realism, New Realism

References[1] R. Kitchin (2014). Big Data, new epistemologies, and paradigm shifts. Big Data &Society, 1(1), 1–12.
[2] M. Fricke (2015). Big Data and Its Epistemology. Journal of the Association for
Information Science and Technology, 66(4),, 651–661.
[3] Q. Meillassoux (2008). After Finitude: An Essay on the Necessity of Contingency.
Continuum, London.
[4] M. Gabriel (2015). Why the World Does Not Exist, Polity, Cambridge.

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Invited Talk: Thursday 15 (11:00-11:45) room RYB2, page: 16

Rank-consistency of the generalized Bradley-Terry modelwith link misspecification
Jongjune Jeona , Yongdai Kimb and Dongha Kimb
a University of Seoul, Korea, b Seoul National University, Korea

The estimation of ranks has received much attention in the areas of information retrieval
and online game design, and the Bradley- Terry model is popularly used. We show thatthe popularity of Bradley- Terry model is due to not only its flexible modeling and easycomputation but also its good asymptotic properties even when the model is misspecified.
We provide sufficient conditions under which the Bradley-Terry model yields a consistentestimate of ranks with partial preferences data when the true generative model of ranks
belongs to the class of the Thurstone model. In addition, we show that the sufficient conditions are almost necessary, discuss how to improve finite sample properties and briefly
consider regression problems.. By numerical experiments, we illustrate that the proposed
sufficient conditions are important and practically useful.
Keywords: Bradley-Terry model, Model Misspecification, Rank-consistency, Thurstonemodel– 143–

Keynote: Thursday 15 (11:50-12:50) room RYB2, page: 16

Data-intensive Discovery and Storytelling using Visualization
Kwan-Liu Ma
University of California at Davis, California, USA

Advances in computing and sensing technologies have led to abundance of data presenting
both challenges and opportunities to various stakeholders, ranging from researchers andpractitioners, to decision and policy makers, and educators and learners. Visualizationtransforms large quantities of, often multi-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging
our brain’s remarkable ability to detect patterns and draw inferences from data. Visualization can be made to present a summary of particular aspects of the data or uncover
new insights in the data. It is thus a powerful, enabling tool for discovering previously
unknowns and telling stories from data. In this talk, I will provide and discuss examples
of designing and using visualization. This will include visualizations designed for diﬀerent
audiences from domain experts to general public as well as for diﬀerent purposes from
exploration to explanation of data found in real-world applications.

– 144–

Index of Authors
A

Aapo Hyvärinen . . . . . . . . . . . . . . . . . . . . . . . 124
Abdurakhman . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Agus Sihabuddin . . . . . . . . . . . . . . . . . . . . . . . 44
Aki-Hiro Sato . . . . . . . . . . . . . . . . . . . . . . . . . 105
Aldy Pradana . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Alfonso Iodice D’Enza . . . . . . . . . . . . . . . . . . 21
Almira Sophie Syamsudin. . . . . . . . . . . . . . .65
Amanda Hering. . . . . . . . . . . . . . . . . . . . . . . . .96
Angelos Markos . . . . . . . . . . . . . . . . . . . . . . . . 21
Atsushi Nitanda . . . . . . . . . . . . . . . . . . . . . . . . 87
Aulia Dini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Avner Bar-Hen . . . . . . . . . . . . . . . . . . . . . . . . . 22
C

Cathy W.S. Chen. . . . . . . . . . . . . . . . . . . . . .135
Chen-An Tsai. . . . . . . . . . . . . . . . . . . . . . . . . . .33
Chi-Wei Lai . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
Chih-Hsuan Lin . . . . . . . . . . . . . . . . . . . . . . . 107
Chih-Hsuan Wu . . . . . . . . . . . . . . . . . . . . . . . 115
Chika Saka . . . . . . . . . . . . . . . . . . . . . . . 130, 131
Ci-Ren Jiang . . . . . . . . . . . . . . . . . . . . . . . . . . 100
D

D. Danardono . . . . . . . . . . . . . . . . . . . . . . . . . . 74
Daisuke Miyamoto. . . . . . . . . . . . . . . . . . . . .130
Danilo Guimarães Gonçalves . . . . . . . . . . . . 38
Dedi Rosadi . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Denis Gracanin . . . . . . . . . . . . . . . . . . . . . . . . 106
Denny Meyer . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Devayanti Anugerahing Husada . . . . . 55, 62
Di Asih I Maruddani . . . . . . . . . . . . . . . . . . . 73
Dianne Cook . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Disty Ridha Hastuti . . . . . . . . . . . . . . . . . . . . 66
Dongha Kim . . . . . . . . . . . . . . . . . . . . . . . . . . 143
E

Grace S. Shieh . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Guan-Hua Huang. . . . . . . . . . . . . . . . . . . . . .107
Gunardi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
H

Haipeng Shen. . . . . . . . . . . . . . . . . . . . . . . . . . .98
Han Ye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Hayafumi Watanabe . . . . . . . . . . . . . . . . . . . . 75
Hee-Seok Oh . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Heng-Hui Lue . . . . . . . . . . . . . . . . . . . . . . . . . . 82
Herni Utami . . . . . . . . . . . . . . . . . . . . . . . . 43, 63
Hideaki Shimazaki . . . . . . . . . . . . . . . . . . . . . 110
Hideo Hirose. . . . . . . . . . . . . . . . . . . . . . . . . . . .93
Hidetoshi Shimodaira . . . . . . . . . . . . . . . . . . . 94
Hiroki Hashiguchi . . . . . . . . . . . . . . . . . . . . . 137
Hiroki Watanabe . . . . . . . . . . . . . . . . . . . . . . 138
Hiroko Katayama . . . . . . . . . . . . . . . . . . . . . . . 42
Hiromichi Sato . . . . . . . . . . . . . . . . . . . . . . . . 112
Hiroshi Nishiyama . . . . . . . . . . . . . . . . . . . . . . 19
Hiroshi Yadohisa . . . . . . . . . . . . . . . . 34, 35, 79
Hiroyasu Abe . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
Hisayuki Hara . . . . . . . . . . . . . . . . . . . . . . . . . 129
Hsin-Cheng Huang . . . . . . . . . . . . . . . . . . . . . 70
Huey-Miin Hsueh . . . . . . . . . . . . . . . . . . . . . . . 49
Huixia Judy Wang . . . . . . . . . . . . . . . . . . . . . . 96
Hung Hung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
I

I-Ping Tu . . . . . . . . . . . . . . . . . . . . . . 25, 40, 101
Ian McKeague . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Inge Koch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Ippei Takasawa . . . . . . . . . . . . . . . . . . . . . . . . . 35
Isamu Nagai . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Ismiyatun Hanifah . . . . . . . . . . . . . . . . . . 47, 62
Isni Sukrianah . . . . . . . . . . . . . . . . . . . . . . . . . . 51J

Elena Ginina . . . . . . . . . . . . . . . . . . . . . . . . . . 106 James Luedtke . . . . . . . . . . . . . . . . . . . . . . . . . 98
Jan-Gowth Chang . . . . . . . . . . . . . . . . . . . . . . 99
FJean-Michel Poggi . . . . . . . . . . . . . . . . . . . . . . 22
Fanghong Zhang . . . . . . . . . . . . . . . . . . . . . . . . 89
Jian-Chi Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Frederick Kin Hing Phoa . . . . . . . . . . . . . . 127
Jim Harner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
Fumio Ishioka . . . . . . . . . . . . . . . . . . . . 102, 103
Jinfang Wang . . . . . . . . . . . . . . . . . . . . . . . . . 140
G
Jongjune Jeaon . . . . . . . . . . . . . . . . . . . . . . . . 143
Genevera Allen . . . . . . . . . . . . . . . . . . . . . . . . 123 Jun Tsuchida . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Genevera I. Allen . . . . . . . . . . . . . . . . . . . . . . 118 Jun-ichiro Hirayama . . . . . . . . . . . . . . . . . . . 124– 147 –

Jungteak Jang . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Masayuki Jimichi . . . . . . . . . . . . . . . . . 130, 131
Junji Nakano . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Michael Beham . . . . . . . . . . . . . . . . . . . . . . . . 106
Michael G. Schimek . . . . . . . . . . . . . . . . . . . . 84
KMichael Weylandt . . . . . . . . . . . . . . . . . . . . . 118
K. Khamthong . . . . . . . . . . . . . . . . . . . . . . . . 135 Michel van de Velden . . . . . . . . . . . . . . . . . . . 21
Kan Hishiyama . . . . . . . . . . . . . . . . . . . . . . . . . 91 Motoaki Kawanabe . . . . . . . . . . . . . . . . . . . . 124
Kazuho Watanabe . . . . . . . . . . . . . . . . . . . . . . 58 Muslim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Kazuki Konda . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Kazuya Fujita . . . . . . . . . . . . . . . . . . . . . . . . . . 92 N
Kazuyuki Koizumi . . . . . . . . . . . . . . . . . . . . . 140 Naoki Makita. . . . . . . . . . . . . . . . . . . . . . . . . .105
Kee-Hoon Kang . . . . . . . . . . . . . . . . . . . . . . . . 64 Naomichi Makino . . . . . . . . . . . . . . . . . . . . . . . 41
Keiji Takai . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Niel Le Roux . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Kenichi Hayashi . . . . . . . . . . . . . . . . . . . . . . . 139 Nobuo Shimizu . . . . . . . . . . . . . . . . . . . . . . . . . 26
Kenichi Satoh . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Noriko Nagata . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Kenji Fukumizu . . . . . . . . . . . . . . . . . . . . . . . 126 Noriyuki Kawano . . . . . . . . . . . . . . . . . . . . . . . 27
Kenji Ogawa . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Nurani Aprilia . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Kensuke Okada . . . . . . . . . . . . . . . . . . . . . 91, 92
OKensuke Tanioka . . . . . . . . . . . . . . . . . . . . 34, 35
Kenta Oono . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 Okito Yamashita . . . . . . . . . . . . . . . . . . . . . . 124
Kohei Adachi . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Olcay Arslan . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Koji Kurihara . . . . . . . . . . . . . . . . . . . . 102, 103 P
Kosuke Ohkusa . . . . . . . . . . . . . . . . . . . . . . . . 121 Patrick J.F. Groenen . . . . . . . . . . . . . . . . . . 136
Kresimir Matkovic . . . . . . . . . . . . . . . . . . . . . 106 Paulo Canas Rodrigues . . . . . . . . . . . . . . . . . 97
Kuan-Hui Wu . . . . . . . . . . . . . . . . . . . . . . . . . . 49
Kuo-Jung Lee . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Q
Kwan-Liu Ma . . . . . . . . . . . . . . . . . . . . . . . . . 144 Qiang He . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
LR

Li-Wei Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Linlin Niu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
Longcheen Huwang . . . . . . . . . . . . . . . . . . . . . 71
Lu-Hung Chen . . . . . . . . . . . . . . . . . . . . . . . . 100
Luca Vitale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Rahmiati Rahim . . . . . . . . . . . . . . . . . . . . . . . . 51
Rainer Splechtna . . . . . . . . . . . . . . . . . . . . . . 106
Raka Andriawan . . . . . . . . . . . . . . . . . . . . 55, 62
Ray Sastri . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Ray-Bing Chen . . . . . . . . . . . . . . . . . . . . . . . . 134
Reynaldi Aries Ariyanto . . . . . . . . . . . . . . . . 55
Rezki M. Sabil . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Richard J. Samworth . . . . . . . . . . . . . . . . . . . 17
Rizky Reza Fauzi . . . . . . . . . . . . . . . . . . . . . . . 76
Roelof Coetzer. . . . . . . . . . . . . . . . . . . . . . . . . .80
Rosyida Widadina Ulya . . . . . . . . . . . . . 65, 66
Ruan Rossouw . . . . . . . . . . . . . . . . . . . . . . . . . . 80
Ryota Imai . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

M

M. Fariz Fadillah Mardianto 43, 47, 54, 55,
62, 65, 66
Madawa W. Jayawardana. . . . . . . . . . . . . .133
Mamoru Amemiya . . . . . . . . . . . . . . . . . . . . . . 72
Mariko Takagishi . . . . . . . . . . . . . . . . . . . . . . 136
Mark Lilback . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
Masahiro Kobayashi . . . . . . . . . . . . . . . . . . . . 58
Masahiro Kuroda . . . . . . . . . . . . . . . . . . . . . . . 42
Masahiro Mizuta . . . . . . . . . . . . . . . . . . . . . . . 28
Masamichi ito . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Masashi Goto. . . . . . . . . . . . . . . . . . . . . . . . . . .77
Masashi Hyodo . . . . . . . . . . . . . . . . . . . . . . . . 138
Masataka Taguri . . . . . . . . . . . . . . . . . . . . . . 140
Masaya Iizuka . . . . . . . . . . . . . . . . . . . . . . . . . . 42

S

S.S.M. Silva . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Senay Ozdemir . . . . . . . . . . . . . . . . . . . . . . . . 122
Servane Gey . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Shang-Ying Shiu. . . . . . . . . . . . . . . . . . . . . . . .23
Shao-Hsuan Wang . . . . . . . . . . . . . . . . . . . . . . 40
Sheng-Hsuan Lin . . . . . . . . . . . . . . . . . . 60, 132– 148–

Sheng-Li Tzeng . . . . . . . . . . . . . . . . . . . . . . . . . 70
Sheng-Mao Chang . . . . . . . . . . . . . . . . . . . . . . 45
Shigekazu Nakagawa . . . . . . . . . . . . . . . . . . . 137
Shingo Takagi . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Shizue Izumi . . . . . . . . . . . . . . . . . . . 27, 30, 141
Sho Hashimoto . . . . . . . . . . . . . . . . . . . . . . . . . 46
Sho Sonoda . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Shoji Kajinishi . . . . . . . . . . . . . . . . . . . . . . . . 103
Shoki Nishimura . . . . . . . . . . . . . . . . . . . . . . . 105
Shuen-Lin Jeng . . . . . . . . . . . . . . . . . . . . . . . . . 39
Shuichi Nagata . . . . . . . . . . . . . . . . . . . . . . . . 130
Shuji Ando . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Shuofan Zhang . . . . . . . . . . . . . . . . . . . . . . . . . 95
Sri Haryatmi Kartiko . . . . . . . . . . . . . . . 43, 54
Subanar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Szu-Han Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
T

W

Wataru Takano . . . . . . . . . . . . . . . . . . . . . . . . . 32
Wei-Hau Chang . . . . . . . . . . . . . . . . . . . . . . . . 40
Wen-Shao Ho. . . . . . . . . . . . . . . . . . . . . . . . . .115
Y

Ya-Mei Chang . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Yaeji Lim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Yanlin Tang . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Yasuyuki Kobayashi . . . . . . . . . . . . . . . . . . . . 68
Yi-Ching Yao . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Ying Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
Ying Sun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Yingying Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Yoko Ono. . . . . . . . . . . . . . . . . . . . . . . . .137, 140
Yongdai Kim . . . . . . . . . . . . . . . . . . . . . . . . . . 143
Yoshihiko Maesono . . . . . . . . . . . . . . . . . . . . . 76
Yoshihiro Hirose . . . . . . . . . . . . . . . . . . . . 37, 38
Yoshihiro Yamazaki. . . . . . . . . . . . . . . . . . . . .31
Yoshikazu Terada. . . . . . . . . . . . . . . . . . . . . . 136
Yoshikazu Yamamoto . . . . . . . . . . . . . . . . . . . 26
Yoshiko Yamane . . . . . . . . . . . . . . . . . . . . . . . 104
Yoshimichi Ochi . . . . . . . . . . . . . . . . . . . . . . . . 77
Yoshiro Yamamoto . . . . . . . . . . . . . . . . . . . . . 29
Yoshiyuki Takeuchi . . . . . . . . . . . . . . . . . . . . 142
Yu-Min Yen . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Yuan-chin Ivan Chang . . . . . . . . . . . . . . . . . 114
Yuejun Zheng . . . . . . . . . . . . . . . . . . . . . . . . . 128
Yuichi Mori. . . . . . . . . . . . . . . . . . . . . . . . . . . . .42
Yuki Morioka . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Yuki Takara . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Yuko Araki . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Yun-Chan Chi . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Yunchan Chi . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Yunita Wulan Sari . . . . . . . . . . . . . . . . . . . . . . 52
Yusuke Matsui. . . . . . . . . . . . . . . . . . . . . . . . . .20
Yusuke Takemura . . . . . . . . . . . . . . . . . . . . . 102
Yuta Tanabe . . . . . . . . . . . . . . . . . . . . . . . . . . 120

Taiji Suzuki . . . . . . . . . . . . . . . . . . . 87, 108, 125
Taiki Shimozono . . . . . . . . . . . . . . . . . . . . . . . . 46
Takahito Shimada . . . . . . . . . . . . . . . . . . . . . 104
Takanori Yoshikawa. . . . . . . . . . . . . . . . . . . . .77
Takashi Seo . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
Takatsugu Yoshioka. . . . . . . . . . . . . . . . . . . . .42
Takayuki Abe . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Takemi Yanagimoto . . . . . . . . . . . . . . . . . . . 121
Takuma Narizuka . . . . . . . . . . . . . . . . . . . . . . . 31
Tasuku Okui . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
Tasuku Yamasaki . . . . . . . . . . . . . . . . . . . . . . . 53
Tetsuji Tonda . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Timothy I. Cannings. . . . . . . . . . . . . . . . . . . .17
Ting-Li Chen . . . . . . . . . . . . . . . . . . 23, 25, 115
Tom van den Berg . . . . . . . . . . . . . . . . . . . . . 136
Tomonori Saitoh. . . . . . . . . . . . . . . . . . . . . . .104
Tomoya Ohyama . . . . . . . . . . . . . . . . . . . . . . . 72
Tomoyuki Naito . . . . . . . . . . . . . . . . . . . . . . . 112
Toshio Honda . . . . . . . . . . . . . . . . . . . . . . . . . 117
Toshiyuki Shimono . . . . . . . . . . . . . . . . . . . . 119
Tsai-Yu Lin. . . . . . . . . . . . . . . . . . . . . . . . . . . . .57
Tso-Jung Yen. . . . . . . . . . . . . . . . . . . . . . . . . .113 Z
Tsuneshi Obata . . . . . . . . . . . . . . . . . . . . . . . . . 30 Zachary Skrivanek . . . . . . . . . . . . . . . . . . . . . . 19
Tsung-Shan Tsou . . . . . . . . . . . . . . . . . . . . . . . 78 Zalfa Nur Amalia . . . . . . . . . . . . . . . . . . . 47, 66
V

Vemmie Nastiti Lestari . . . . . . . . . . . . . . . . . 48
Very Basuki . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Vesa Kiviniemi . . . . . . . . . . . . . . . . . . . . . . . . 124– 149–

               8:15 - 9:00
Registration
9:00 - 9:30
Opening Ceremony
9:30 Keynote1:
10:30
Richard J. Samworth
10:30 - 11:00
Coffee Break
11:00 ITS_1 CPS_1 CPS_2 CPS_3
12:30
12:30 - 13:40
Lunch Break
13:40 ShortCPS_4 CPS_5 CPS_6
14:20 Oral
14:30 Poster CPS_7 CPS_8
15:50
15:50 - 16:20
Coffee Break
16:20 ITS_2 CPS_9 CPS_10 CPS_11
17:20
17:25 Invited Talk1:
18:10
Hidetoshi Shimodaira
18:15 - 19:30
Move by Courtesy Bus
19:30 - 21:00
Welcome Reception 
  

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9:00 10:00
10:00 -

Keynote2:
Di CookInvited Talk2:
Huixia Judy Wang
Coffee Break

10:45
10:45 - 11:15
11:15 ITS_3 ITS_6 CPS_12 CPS_13
12:45
12:45 - 13:55
Lunch Break
13:55 ITS_4 ITS_7 CPS_14 CPS_15
15:25
15:25 - 15:55
Coffee Break
15:55 Invited Talk3:
16:40
Genevera Allen
16:45 ITS_5 ITS_8 CPS_16
18:15
18:15 - 19:30
Move by Courtesy Bus
19:30 Conference Dinner
21:30  




             
9:00 10:30
10:30 - 11:00
11:00 11:45
11:50 12:50
12:50 - 13:05

ITS_9

   

CPS_17

CPS_18Coffee BreakInvited Talk4:
Yongdai Kim
Keynote3:
Kwan-Liu Ma
Closing Ceremony  
