Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood

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CRC Press, 2006. 7. 13. - 416페이지
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Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.

Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives.

Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.
 

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목차

Introduction
1
Classical likelihood theory
5
Generalized Linear Models
37
Quasilikelihood
65
Extended Likelihood Inferences
97
Normal linear mixed models
135
Hierarchical GLMs
173
HGLMs with structured dispersion
203
Smoothing
267
Randomeffect models for survival data
293
Double HGLMs
319
Further topics
343
References
363
Data Index
380
Author Index
381
Subject Index
385

Correlated random effects for HGLMs
231

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