Linear and Generalized Linear Mixed Models and Their Applications

  • Jiming┬áJiang
Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Back Matter
    Pages 231-257

About this book

Introduction

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.

The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis. The book is suitable for a course in a M.S. program in statistics, provided that the section of further results and technical notes in each of the first four chapters is skipped. If these four sections are included, the book may be used for a course in a Ph. D. program in statistics. A first course in mathematical statistics, the ability to use computers for data analysis, and familiarity with calculus and linear algebra are prerequisites. Additional statistical courses such as regression analysis and a good knowledge about matrices would be helpful.

Jiming Jiang is Professor of Statistics and Director of the Statistical Laboratory at UC-Davis. He is a prominent researcher in the fields of mixed effects models and small area estimation, and co-receiver of the Chinese National Natural Science Award and American Statistical Association's Outstanding Statistical Application Award.

Keywords

Regression analysis data analysis generalized linear mixed models linear mixed models linear optimization mathematical statistics model selection prediction random effects

Authors and affiliations

  • Jiming┬áJiang
    • 1
  1. 1.Department of StatisticsUniversity of California, DavisDavis

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-387-47946-0
  • Copyright Information Springer Science + Business Media, LLC 2007
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-0-387-47941-5
  • Online ISBN 978-0-387-47946-0
  • Series Print ISSN 0172-7397
  • About this book