Overview
- Features exercises and real examples throughout, to ensure retention of information
- Offers an up-to-date account of theory and methods in the analysis of these models as well as their applications in various fields
- Provides a comprehensive coverage of linear mixed models and generalized linear mixed models
Part of the book series: Springer Series in Statistics (SSS)
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About this book
Now in its second edition, 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. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.
This book is suitable for students, researchers, and practitioners who are interested in using mixed models for statistical data analysis with public health applications. It is best for graduate courses in statistics, or for those who have taken a first course in mathematical statistics, are familiar with using computers for data analysis, and have a foundational background in calculus and linear algebra.
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Keywords
Table of contents (4 chapters)
Authors and Affiliations
About the authors
Jiming Jiang is Professor of Statistics and a former Director of Statistical Laboratory at the University of California, Davis. He is a prominent researcher in the fields of mixed effects models, small area estimation, model selection, and statistical genetics. He is the author of Large Sample Techniques for Statistics (Springer 2010), Robust Mixed Model Analysis (2019), Asymptotic Analysis of Mixed Effects Models: Theory, Applications, and Open Problems (2017), and The Fence Methods (with T. Nguyen, 2016). He has been editorial board member of The Annals of Statistics and Journal of the American Statistical Association, among others. He is a Fellow of the American Association for the Advancement of Science, the American Statistical Association, and the Institute of Mathematical Statistics; an elected member of the International Statistical Institute; and a Yangtze River Scholar (Chaired Professor, 2017-2020).
Thuan Nguyen is Associate Professor of Biostatistics in the School of Public Health at Oregon Health & Science University, where she teaches and advises graduate students. She is an active researcher in the field of biostatistics, specializing in the analysis of longitudinal data and statistical genetics, as well as small area estimation. She is the coauthor of The Fence Methods (with J. Jiang 2016).
Bibliographic Information
Book Title: Linear and Generalized Linear Mixed Models and Their Applications
Authors: Jiming Jiang, Thuan Nguyen
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-1-0716-1282-8
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2021
Hardcover ISBN: 978-1-0716-1281-1Published: 23 March 2021
Softcover ISBN: 978-1-0716-1284-2Published: 23 March 2022
eBook ISBN: 978-1-0716-1282-8Published: 22 March 2021
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 2
Number of Pages: XIV, 343
Number of Illustrations: 5 b/w illustrations, 8 illustrations in colour
Topics: Statistics for Life Sciences, Medicine, Health Sciences, Probability Theory and Stochastic Processes, Statistical Theory and Methods, Public Health, Numerical Analysis, Genetics and Population Dynamics