Youngjo Lee, John A. Nelder, Yudi Pawitan

Generalized Linear Models with Random Effects (eBook, ePUB)

Unified Analysis via H-likelihood, Second Edition

• Format: ePub

Produktbeschreibung

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in variable selection and multiple testing, and new examples and applications. It includes an R package for all the methods and examples that supplement the book.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 466
• Erscheinungstermin: 11. Juli 2018
• Englisch
• ISBN-13: 9781351646260
• Artikelnr.: 68423766

Autorenporträt

Youngjo Lee is Professor at Seoul National University, South Korea.

Inhaltsangabe

Preface to the first edition
Preface
Introduction
Classical likelihood theory
Generlized linear models
Quasi-likelihood
Extended likelihood inferences
Normal linear mixed models
Hierarchical GLMS
HGLMs with structured dispersion
Correlated randoms effects for HGLMs
Smoothing
Double HGLMs
Variable Selection and Sparsity Models
Multivariate and Missing Data Analysis
Multiple testing
References
Data index
Author index
Subject index

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Preface to the first edition

Preface

Introduction

Classical likelihood theory

Generlized linear models

Quasi-likelihood

Extended likelihood inferences

Normal linear mixed models

Hierarchical GLMS

HGLMs with structured dispersion

Correlated randoms effects for HGLMs

Smoothing

Double HGLMs

Variable Selection and Sparsity Models

Multivariate and Missing Data Analysis

Multiple testing

References

Data index

Author index

Subject index

Rezensionen

"Generalized Linear Models with Random Effects is a comprehensive book on likelihood methods in generalized linear models (GLMs) including linear models with normally distributed errors. ... The book is suitable for those with graduate training in mathematical statistics. The level of mathematical detail is similar to that of McCullagh and Nelder (1989), with the focus shifted towards likelihood methods. All chapters contain examples with a fair amount of detail. The book is very broad and offers a comprehensive overview of likelihood methods."
-Christiana Drake, in ISCB News, December 2018

Praise for the first edition:

"... This book provides a comprehensive summary of [the authors' past work]. However, it is much more than that, and even statisticians who do not agree with their approach to inference will find much here of interest. ... some instructors might find this to be a useful text for a course on generalized linear models. ... there are many ideas that will be useful for students to mull over ..."
- A. Agresti (University of Florida), Short Book Reviews

"The book is well written and replete with examples and discussions. With over 500 references, the authors have amassed an enormous amount of information in a single source."
- James W. Hardin, University of South Carolina, in Journal of the American Statistical Association, June 2009, Vol. 104, No. 486

"The book's material is valuable . . . There are numerous examples and applications, illustrated on the accompanying Genstat CD."
- Hassan S. Bakouch, Tanta University, in Journal of Applied Statistics, September 2007, Vol. 34, No. 7