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Regression graph models for categorical data = parameterization and inference /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Regression graph models for categorical data/ by Monia Lupparelli, Giovanni Maria Marchetti, Claudia Tarantola.
Reminder of title:	parameterization and inference /
Author:	Lupparelli, Monia.
other author:	Marchetti, Giovanni Maria.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xii, 109 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Preface -- 1 Regression Graph Models -- 2 Multivariate Logistic Regression Models -- 3 Maximum Likelihood Inference -- 5 Bayesian Inference -- References -- Index.
Contained By:	Springer Nature eBook
Subject:	Regression analysis. -
Online resource:	https://doi.org/10.1007/978-3-031-99797-6
ISBN:	9783031997976

Regression graph models for categorical data = parameterization and inference /
Lupparelli, Monia.

Regression graph models for categorical dataparameterization and inference /[electronic resource] :by Monia Lupparelli, Giovanni Maria Marchetti, Claudia Tarantola. - Cham :Springer Nature Switzerland :2025. - xii, 109 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in statistics,2191-5458. - SpringerBriefs in statistics..

Preface -- 1 Regression Graph Models -- 2 Multivariate Logistic Regression Models -- 3 Maximum Likelihood Inference -- 5 Bayesian Inference -- References -- Index.

This book consolidates knowledge on regression chain graph models, often referred to as regression graph models, with a particular emphasis on their parameterizations and inference for the analysis of categorical data. It presents regression graphs, their interpretation in terms of sequences of multivariate regressions, interpretable parameterizations for categorical data, and inference and model selection within the frequentist and Bayesian approaches. The aim is to reveal the benefits of this family of graphical models for statistical data analysis and to encourage applications of these models as well as further research in the field. Data and R code used in the book are available online. The text is primarily intended for graduate and PhD students in statistics and data science who are familiar with the basics of graphical Markov models and of categorical data analysis, and for motivated researchers in specific applied fields.

ISBN: 9783031997976

Standard No.: 10.1007/978-3-031-99797-6doiSubjects--Topical Terms:

529831
Regression analysis.

LC Class. No.: QA278.2

Dewey Class. No.: 519.536

Regression graph models for categorical data = parameterization and inference /
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505 0 $a Preface -- 1 Regression Graph Models -- 2 Multivariate Logistic Regression Models -- 3 Maximum Likelihood Inference -- 5 Bayesian Inference -- References -- Index.
520 $a This book consolidates knowledge on regression chain graph models, often referred to as regression graph models, with a particular emphasis on their parameterizations and inference for the analysis of categorical data. It presents regression graphs, their interpretation in terms of sequences of multivariate regressions, interpretable parameterizations for categorical data, and inference and model selection within the frequentist and Bayesian approaches. The aim is to reveal the benefits of this family of graphical models for statistical data analysis and to encourage applications of these models as well as further research in the field. Data and R code used in the book are available online. The text is primarily intended for graduate and PhD students in statistics and data science who are familiar with the basics of graphical Markov models and of categorical data analysis, and for motivated researchers in specific applied fields.
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