New Statistical Developments in Data Science [electronic resource] : SIS 2017, Florence, Italy, June 28-30 /
Petrucci, Alessandra.editor.
Racioppi, Filomena.editor.
Verde, Rosanna.editor.
This volume collects the extended versions of papers presented at the SIS Conference “Statistics and Data Science: new challenges, new generations”, held in Florence, Italy on June 28-30, 2017. Highlighting the central role of statistics and data analysis methods in the era of Data Science, the contributions offer an essential overview of the latest developments in various areas of statistics research. The 35 contributions have been divided into six parts, each of which focuses on a core area contributing to “Data Science”. The book covers topics including strong statistical methodologies, Bayesian approaches, applications in population and social studies, studies in economics and finance, techniques of sample design and mathematical statistics. Though the book is mainly intended for researchers interested in the latest frontiers of Statistics and Data Analysis, it also offers valuable supplementary material for students of the disciplines dealt with here. Lastly, it will help Statisticians and Data Scientists recognize their counterparts’ fundamental role.
PART I – Complex data analytics: A. Balzanella et al., Monitoring the spatial correlation among functional data streams through Moran's Index -- O. Banouar and S. Raghay, User query enrichment for personalized access to data through ontologies using matrix completion method -- C. Drago, Clustering Communities using Interval K-Means -- F. Murtagh, Text Mining and Big Textual Data: Relevant Statistical Models -- G. Ragozini et al., A three-way data analysis approach for analyzing multiplex networks -- M. Ruggieri et al., Comparing FPCA based on conditional quantile functions and FPCA based on conditional mean function -- F. Santelli et al., Statistical archetypal analysis for cognitive categorization -- A. Vanacore and M. S. Pellegrino, Inferring rater agreement with ordinal classification -- PART II – Knowledge based methods: J. Koskinen et al., Bayesian analysis of ERG models for multilevel, multiplex, and multilayered networks with sampled or missing data -- C. Scricciolo, Bayesian Kantorovich Deconvolution in Finite Mixture Models -- A. Sottosanti et al., Discovering and Locating High-Energy Extra-Galactic\Sources by Bayesian Mixture Modelling -- F. Stefanini and G. Callegaro, Bayesian estimation of causal effects in carcinogenicity tests based upon CTA -- M. Subbiah et al., Performance Comparison of Heterogeneity Measures for count data models in Bayesian Perspective -- PART III – Sampling techniques for Big Data exploration: M. S. Andreano et al., Sampling and modelling issues using Big Data in now–casting -- M. D’Alò et al., Sample design for the integration of population census and social surveys -- C. De Vitiis et al., Sampling schemes using scanner data for the consumer price index -- S. Polettini and S. Arima, An investigation of Hierarchical and Empirical Bayesian small area predictors under measurement error -- E. Rocco, Indicators for monitoring the survey data quality when non-response or a convenience sample occurs -- PART IV – Data Science methods for social and population studies: P. Balduzzi et al., The propensity to leave the country of origin of young Europeans -- F. Bassi et al., New Insights on Student Evaluation of Teaching in Italy -- A. Bikauskaite and D. Buono, Eurostat methodological network: Skills mapping for a collaborative statistical office -- M. Costa, The evaluation of the inequality between population subgroups -- M. Manisera et al., Basketball analytics using spatial tracking data -- I. Morlini and M. Scorza, New fuzzy composite indicators for dyslexia -- A. Righi et al., Who tweets in Italian? Demographic characteristics of Twitter users -- PART V – Applying Data Science in economics and labour market: A. Agapitov et al., An approach to developing a scoring system for peer-to-peer (p2p) lending platform -- P. Mariani et al., What do employers look for when hiring new graduates? Answers from the Electus survey -- A. Mazza and A. Punzo, Modeling Household Income with Contaminated Unimodal Distributions -- G. Punzo et al., Endowments and rewards in the labour market: their role in changing wage inequality in Europe -- V. Voytsekhovska and O. Butzbach, The approach towards the analysis of wage distribution equality dynamics in Poland based on linear dependences -- PART VI – Mathematical statistics for Data Science: R. Fontana and F. Rapallo, Unions of Orthogonal Arrays and their aberrations via Hilbert bases -- F. Lagona, A copula-based hidden Markov model for toroidal time Series -- A. Lanteri et al., A biased Kaczmarz algorithm for clustered equations -- A. Lepore, Nearly Unbiased Probability Plots for Extreme Value Distributions -- V. Mameli et al., Estimating High-Dimensional Regression Models with Bootstrap group Penalties.
This volume collects the extended versions of papers presented at the SIS Conference “Statistics and Data Science: new challenges, new generations”, held in Florence, Italy on June 28-30, 2017. Highlighting the central role of statistics and data analysis methods in the era of Data Science, the contributions offer an essential overview of the latest developments in various areas of statistics research. The 35 contributions have been divided into six parts, each of which focuses on a core area contributing to “Data Science”. The book covers topics including strong statistical methodologies, Bayesian approaches, applications in population and social studies, studies in economics and finance, techniques of sample design and mathematical statistics. Though the book is mainly intended for researchers interested in the latest frontiers of Statistics and Data Analysis, it also offers valuable supplementary material for students of the disciplines dealt with here. Lastly, it will help Statisticians and Data Scientists recognize their counterparts’ fundamental role.
Statistics .
Computer mathematics.
Business enterprises—Finance.
Statistics and Computing/Statistics Programs.
Computational Mathematics and Numerical Analysis.
Business Finance.
Statistics for Business, Management, Economics, Finance, Insurance.
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.