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

Youngjo Lee, John A. Nelder, Yudi Pawitan

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July 13, 2006 by Chapman and Hall/CRC
Reference - 416 Pages - 54 B/W Illustrations
ISBN 9781420011340 - CAT# CE6315
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

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Features

  • Presents new methods for fitting GLMs with random effects
  • Provides many models in a single framework with a single algorithm for fitting, reducing complex structures to simple components
  • Includes background material on likelihood inference and GLMs
  • Computes methods using Genstat, with datasets and software available on CD and online, making it easy to test alternative analyses
  • Displays aspects of the model class through examples that cover a variety of applications, including medicine, finance, epidemiology, and agriculture
  • Summary

    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.