Trends and challenges in categorical data analysis : statistical modelling and interpretation / edited by Maria Kateri, Irini Moustaki.
Kateri, Maria, editor.; Moustaki, Irini, editor.
2023
QA278
Available Online

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Title
Trends and challenges in categorical data analysis : statistical modelling and interpretation / edited by Maria Kateri, Irini Moustaki.
ISBN
9783031311864 (electronic bk.)
3031311868 (electronic bk.)
9783031311857
303131185X
DOI
https://doi.org/10.1007/978-3-031-31186-4
Published
Cham : Springer, 2023.
Language
English
Description
1 online resource (340 pages) : illustrations (black and white, and colour).
Item Number
10.1007/978-3-031-31186-4 doi
Call Number
QA278
Dewey Decimal Classification
519.5/35
Summary
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.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Kateri, Maria, editor.
Moustaki, Irini, editor.
Series
Statistics for social and behavioral sciences.
Available in Other Form
Trends and challenges in categorical data analysis.
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Online Access
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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. Tamas 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.

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