Statistical Learning of Complex Data. 1st ed. 2019

種類:

電子ブック

責任表示:

edited by Francesca Greselin, Laura Deldossi, Luca Bagnato, Maurizio Vichi

出版情報:

Cham : Springer International Publishing : Imprint: Springer, 2019

著者名:

• Greselin, Francesca.
• Deldossi, Laura.
• Bagnato, Luca.
• Vichi, Maurizio.
• SpringerLink (Online service)

シリーズ名:

Studies in Classification, Data Analysis, and Knowledge Organization ;

ISBN:

9783030211400 [3030211401]    

注記:

Preface -- Contributors -- Part I Clustering and Classification -- 1.1 Cluster Weighted Beta Regression: a simulation study -- 1.2 Detecting wine adulterations employing robust mixture of Factor Analyzers -- 1.3 Simultaneous supervised and unsupervised classification modeling for assessing cluster analysis and improving results interpretability -- 1.4 A parametric version of probabilistic distance clustering -- 1.5 An overview on the URV Model-Based Approach to Cluster Mixed-Type Data -- Part II Exploratory Data Analysis -- 2.1 Preference Analysis of Architectural Facades by Multidimensional Scaling and Unfolding -- 2.2 Community Structure in Co-authorship Networks: the Case of Italian Statisticians -- 2.3 Analyzing Consumers’ Behaviour in Brand Switching -- 2.4 Evaluating the Quality of Data Imputation in Cardiovascular Risk studies Through the Dissimilarity Profile Analysis -- Part III Statistical Modeling -- 3.1 Measuring Economic Vulnerability: a Structural Equation Modeling Approach -- 3.2 Bayesian Infer
This book of peer-reviewed contributions presents the latest findings in classification, statistical learning, data analysis and related areas, including supervised and unsupervised classification, clustering, statistical analysis of mixed-type data, big data analysis, statistical modeling, graphical models and social networks. It covers both methodological aspects as well as applications to a wide range of fields such as economics, architecture, medicine, data management, consumer behavior and the gender gap. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification. It gathers selected and peer-reviewed contributions presented at the 11th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical S

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