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Trends and challenges in categorical data analysisstatistical modelling and interpretation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Trends and challenges in categorical data analysisedited by Maria Kateri, Irini Moustaki.
Reminder of title:	statistical modelling and interpretation /
other author:	Kateri, Maria.
Published:	Cham :Springer International Publishing :2023.
Description:	xii, 315 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Statistical Theory and Methods.
Online resource:	https://doi.org/10.1007/978-3-031-31186-4
ISBN:	9783031311864$q(electronic bk.)

Trends and challenges in categorical data analysisstatistical modelling and interpretation /
Trends and challenges in categorical data analysisstatistical modelling and interpretation /[electronic resource] :edited by Maria Kateri, Irini Moustaki. - Cham :Springer International Publishing :2023. - xii, 315 p. :ill., digital ;24 cm. - Statistics for social and behavioral sciences,2199-7365. - Statistics for social and behavioral sciences..

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.

ISBN: 9783031311864$q(electronic bk.)

Standard No.: 10.1007/978-3-031-31186-4doiSubjects--Topical Terms:

274054
Statistical Theory and Methods.

LC Class. No.: QA278

Dewey Class. No.: 519.535

Trends and challenges in categorical data analysisstatistical modelling and interpretation /
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505 0 $a 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.
520 $a 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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