International Symposium on Recent Advances in Theories andMethodologies for Large Complex Data
December 7-9, 2023
Venue:
Conference Room 101, Tsukuba International Congress Center
2-20-3 Takezono, Tsukuba, Ibaraki 305-0032, Japan (Hybrid Symposium with Zoom)
Organizers:
Makoto Aoshima (University of Tsukuba)
Kazuyoshi Yata

(University of Tsukuba)

Aki Ishii(Tokyo University of Science)

Kento Egashira(Tokyo University of Science)

Supported by
Grant-in-Aid for Scientiﬁc Research (A) 20H00576 (Project Period: 2020-2024)“Innovative developments of theories and methodologies for large complex data”(Principal Investigator: Makoto Aoshima)
Grant-in-Aid for Challenging Research (Exploratory) 22K19769 (Project Period: 2022-2024)“Developments of statistical compression technology for massive data having tensor structures”(Principal Investigator: Makoto Aoshima)

Program (UTC+9)
December 7 (Thursday)
13:50∼14:00

Opening

14:00∼14:40

Kento Egashira∗,a , Kazuyoshi Yatab and Makoto Aoshimabab(Department of Information Sciences, Tokyo University of Science)(Institute of Mathematics, University of Tsukuba)

Asymptotic properties of kernel k-means for high dimensional data
14:50∼15:30

Yohji Akama(Mathematical Institute, Tohoku University)

Broken-stick components retention rule for equi-correlated normal population
15:40∼16:20

Takayuki Morimoto(School of Science, Kwansei Gakuin University)

Forecasting high-dimensional covariance matrices using high-dimensional
principal component analysis

16:30∼17:10

Shao-Hsuan Wang
(Graduate Institute of Statistics, National Central University)

A geometric algorithm for contrastive principal component analysis in high dimension
17:20∼18:00

Taiji Suzuki∗,a , Denny Wub , Atsushi Nitandac and Kazusato Okoa(Zoom)a(Department of Mathematical Informatics, The University of Tokyo / RIKEN AIP)b(Center for Data Science, New York University)c(Department of Artiﬁcial Intelligence, Kyushu Institute of Technology / RIKEN AIP)

Feature learning via mean ﬁeld neural networks and anisotropic features

December 8 (Friday)
9:00∼9:40

Akifumi Okuno(The Institute of Statistical Mathematics / RIKEN AIP)

Statistical estimation with integral-based loss functions
9:50∼10:30

Masaaki Imaizumi(Komaba Institute for Science, The University of Tokyo / RIKEN AIP)

Non-sparse high-dimensional statistics and its applications
10:40∼11:20

Tsutomu T. Takeuchi(Division of Particle and Astrophysical Science, Nagoya University)

Statistical challenges to dimensionality in astronomical big data
11:30∼12:10

Yuan-Tsung Chang∗,a , Nobuo Shinozakib and William, E. Strawdermanca(Department of Social Information, Mejiro University)b(Faculty of Science and Technology, Keio University)c(Department of Statistics, Rutgers University)

Predictive density estimation for two ordered normal means under α-divergence loss
12:10∼13:40

Lunch

13:40∼18:00

Special Invited and Keynote Sessions

19:00∼21:00

DinnerDecember 9 (Saturday)
9:00∼9:40

Shogo Nakakita(Komaba Institute for Science, The University of Tokyo)

On approximate sampling from non-log-concave non-smooth distributions
via a Langevin-type Monte Carlo algorithm

9:50∼10:30

Kou Fujimori∗,a and Koji Tsukudabab(Department of Economics, Shinshu University)(Faculty of Mathematics, Kyushu University)

Two step estimations via the Dantzig selector for ergodic time series models
10:40∼11:20

Junichi Hirukawa∗,a and Kou Fujimoribab(Faculty of Science, Niigata University)(Department of Economics, Shinshu University)

Innovation algorithm of fractionally integrated (I(d)) process and applications
on the estimation of parameters
11:30∼12:10

Kengo Kamatani(The Institute of Statistical Mathematics)

Scaling limits of Markov chains/processes in Monte Carlo methods
12:20∼13:00

Takahiro Nishiyama∗,a and Masashi Hyodobab(Department of Business Administration, Senshu University)(Department of Economics, Kanagawa University)

On a general linear hypothesis testing problem for latent factor models
in high dimensions
13:00∼13:10

Closing(∗ Speaker)

Special Invited and Keynote Sessions
December 8 (Friday)
Special Invited Session
13:40∼14:30

On the eﬃciency-loss free ordering-robustness of product-PCA

Speaker: Hung Hung(Institute of Health Data Analytics and Statistics, National Taiwan University)
Discussion Leader: Yuan-Tsung Chang

14:40∼15:30(Department of Social Information, Mejiro University)

Learning ordinality in high-dimensional data

Speaker: Jeongyoun Ahn(Department of Industrial and Systems Engineering, KAIST)
Discussion Leader: Kazuyoshi Yata(Institute of Mathematics, University of Tsukuba)

Keynote Session
15:50∼16:50

Normal-reference test for high-dimensional covariance matrices

Speaker: Jin-Ting Zhang(Department of Statistics and Data Science, National University of Singapore)
Discussion Leader: Aki Ishii

17:00∼18:00(Department of Information Sciences, Tokyo University of Science)

Testing high-dimensional general linear hypotheses through spectral shrinkage

Speaker: Debashis Paul(Zoom)(Department of Statistics, University of California, Davis / Indian Statistical Institute, Kolkata)

Discussion Leader: Yuta Koike(Graduate School of Mathematical Sciences, The University of Tokyo)