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Linear and Generalized Linear Mixed Models and Their Applications

  • Book
  • © 2007

Overview

Authors:
  1. Jiming Jiang

    1. Department of Statistics, University of California, Davis, Davis

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  • Concentrates on two major classes of mixed effects models, linear mixed models and generalized linear mixed models
  • Offers an up-to-date account of theory and methods in the analysis of these models as well as their applications in various fields
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Series in Statistics (SSS)

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Table of contents (4 chapters)

  1. Front Matter

    Pages I-XIV
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  2. Linear Mixed Models: Part I

    Pages 1-50

  3. Linear Mixed Models: Part II

    Pages 51-117

  4. Generalized Linear Mixed Models: Part I

    Pages 119-162

  5. Generalized Linear Mixed Models: Part II

    Pages 163-230

  6. Back Matter

    Pages 231-257
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Keywords

About this book

Over the past decade there has been an explosion of developments in mixed e?ects models and their applications. This book concentrates on two major classes of mixed e?ects models, linear mixed models and generalized linear mixed models, with the intention of o?ering an up-to-date account of theory and methods in the analysis of these models as well as their applications in various ?elds. The ?rst two chapters are devoted to linear mixed models. We classify l- ear mixed models as Gaussian (linear) mixed models and non-Gaussian linear mixed models. There have been extensive studies in estimation in Gaussian mixed models as well as tests and con?dence intervals. On the other hand, the literature on non-Gaussian linear mixed models is much less extensive, partially because of the di?culties in inference about these models. However, non-Gaussian linear mixed models are important because, in practice, one is never certain that normality holds. This book o?ers a systematic approach to inference about non-Gaussian linear mixed models. In particular, it has included recently developed methods, such as partially observed information, iterative weighted least squares, and jackknife in the context of mixed models. Other new methods introduced in this book include goodness-of-?t tests, p- diction intervals, and mixed model selection. These are, of course, in addition to traditional topics such as maximum likelihood and restricted maximum likelihood in Gaussian mixed models.

Reviews

From the reviews:

"This book is an up to date description of linear mixed models, LMM, and generalized linear mixed models, GLMM. The material is complete enough to cover a course in a Ph.D. program in statistics. The contribution of this book is that of pointing and developing the inference and estimation issues for non-Gaussion LMMs." (Nicoleta Breaz, Zentrablatt MATH, 2009, 1152)

"The book deals with Gaussian and non-Gaussian linear mixed models. … This book is suitable for a course in statistics at the MSc level … . This book contains many examples, exercises and some useful appendices, making it suitable for use in statistics courses. … The book has a nice lay-out and the index make it easy to jump to a topic of interest. … A nice feature of the book are the many real-life data examples." (M. Moerbeek, Kwantitatieve Methoden, August, 2007)

"This book, which has grown out of the author's research on this area, deserves close attention. It provides a good reference source for an advanced graduate course and would prove useful for research workers who wish to learn about theoretical developments in this area...[T]his book will be a useful source for obtaining the theoreteical skills required for further developments in this area." (Youngjo Lee, Biometrics, December 2007)

"As noted by the author, there have been many new developments in mixed effects models in the past decade. This volume is intended to provide an up-to-date treatment of both theory and methods. … it is full of important results and examples, including significant contributions by the author to the treatment of mixed effects models. As a textbook, it is aimed at MS students in statistics, but includes supplementary material more suitable for PhD candidates. … be useful as such for many GLMM users." (Donald E. Myers, Technometrics, Vol. 50 (1), 2008)

"The book under review covers both LMMs and GLMMs and offersan up-to-date account of theory and methods in the analysis of the models as well as their applications in biological and the medical research, animal and human genetics, and small area estimation. The examples of applications appear near the end of each chapter. … The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis." (Alexander G. Kukush, Mathematical Reviews, Issue 2007 m)

"Jiming Jiang’s book on mixed models covers a lot of material in surprisingly few pages. … On the whole, it is a rather technical book, both in terms of the mathematical level and in terms of notation … . Undoubtedly there is a lot one may learn from the book … . useful for the professional who wishes to know more about the technical results of the topic … ." (Søren Feodor Nielsen, Journal of Applied Statistics, Vol. 35 (4), 2008)

Authors and Affiliations

  • Department of Statistics, University of California, Davis, Davis

    Jiming Jiang

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