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＜電子ブック＞
Trends and Challenges in Categorical Data Analysis : Statistical Modelling and Interpretation / edited by Maria Kateri, Irini Moustaki
(Statistics for Social and Behavioral Sciences. ISSN:21997365)

版	1st ed. 2023.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2023
本文言語	英語
大きさ	XII, 315 p. 57 illus., 11 illus. in color : online resource
著者標目	Kateri, Maria editor Moustaki, Irini editor SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Biometry LCSH:Psychometrics LCSH:Epidemiology FREE:Statistical Theory and Methods FREE:Biostatistics FREE:Psychometrics FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences FREE:Epidemiology
一般注記	Preface -- Chapter 1. Carolyn J. Anderson, Maria Kateri and Irini Moustaki: Log-Linear and Log-Multiplicative Association Models for Categorical Data -- Chapter 2. Peter W. F. Smith: Graphical Models for Categorical Data -- Chapter 3. Tam´as Rudas and Wicher Bergsma: Marginal Models: an Overview -- Chapter 4. Jonathan J Forster and Mark E Grigsby: Bayesian Inference for Multivariate Categorical Data -- Chapter 5. Alan Agresti, Claudia Tarantola and Roberta Varriale: Simple Ways to Interpret Effects in Modeling Binary Data -- Chapter 6. Ioannis Kosmidis: Mean and median bias reduction: A concise review and application to adjacent-categories logit models -- Chapter 7. Jan Gertheiss and Gerhard Tutz: Regularization and Predictor Selection for Ordinal and Categorical Data -- Chapter 8. Mirko Armillotta, Alessandra Luati and Monia Lupparelli: An overview of ARMA-like models for count and binary data -- Chapter 9. Francesco Valentini, Claudia Pigini, and Francesco Bartolucci: Advances in maximum likelihood estimation of fixed-effects binary panel data models This book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences HTTP:URL=https://doi.org/10.1007/978-3-031-31186-4

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