Statistical Learning of Complex Data
Springer International Publishing (Verlag)
978-3-030-21139-4 (ISBN)
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 Society (CLADAG 2017), held in Milan, Italy, on September 13-15, 2017.Francesca Greselin is an Associate Professor of Statistics at the University of Milano-Bicocca, Milan, Italy. She teaches Statistics and Insurance Risks for graduate students and Inference for PhD students. Her research interests range from robust statistical methods for model-based classification and clustering, to inferential results for inequality and risk measures. She has published more than 30 scientific papers in peer-reviewed international statistics journals. Laura Deldossi is an Associate Professor of Statistics at the Università Cattolica del Sacro Cuore in Milan, Italy. Her main research interests are optimal design of experiments, Bayesian model discrimination, discrete choice models, experimental and quasi-experimental design for causal inference designs, and statistical process control. She has taught several courses: Statistics, Applied Statistics, Data Analysis and Sample Techniques, and Design of Experiments. Luca Bagnato is an Assistant Professor of Statistics at the Università Cattolica del Sacro Cuore in Piacenza, Italy. He completed his Ph.D. in Statistics at the University of Milano-Bicocca in 2009 and received two postdoctoral fellowships: at the University of Milano-Bicocca and at the University of Verona. His research interests include time series analysis, distribution theory, mixture models, and spatial statistics. He has published more than 20 scientific papers in peer-reviewed journals.
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 Inference for a Mixture Model on the Simplex.- 3.3 Stochastic Models for the Size Distribution of Italian Firms: A Proposal.- 3.4 Modeling Return to Education in Heterogeneous Populations. An application to Italy.- 3.5 Changes in Couples' Bread-winning Patterns and Wife's Economic Role in Japan from 1985 to 2015.- 3.6 Weighted Optimization with Thresholding for Complete-Case Analysis.- Part IV Graphical Models.- 4.1 Measurement Error Correction by NonParametric Bayesian Networks: Application and Evaluation.- 4.2 Copula Grow-Shrink Algorithm for Structural Learning.- 4.3 Context-Specific Independencies Embedded in Chain Graph Models of Type I.- Part V Big Data Analysis.- 5.1 Big Data and Network Analysis: A combined Approach to Model Online News.- 5.2 Experimental Design Issues in Big Data. The Question of Bias.
| Erscheinungsdatum | 09.09.2019 |
|---|---|
| Reihe/Serie | Studies in Classification, Data Analysis, and Knowledge Organization |
| Zusatzinfo | XIII, 201 p. 37 illus., 11 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 335 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | Big Data • classification • Clustering • Complex Data • Data Analysis • explanatory data analysis • Functional Data • Graphical Models • machine learning methods • Multidimensional Scaling • multiway data • network data • pattern recognition • Statistical Learning • statistical modeling |
| ISBN-10 | 3-030-21139-8 / 3030211398 |
| ISBN-13 | 978-3-030-21139-4 / 9783030211394 |
| Zustand | Neuware |
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