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

Autorentext

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).

Zusammenfassung
This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. Furthermore, it includes recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models.

Inhalt

1. Linear Mixed Models: Part I.- 2. Linear Mixed Models: Part II.- 3. Generalized Linear Mixed Models: Part I.- 4. Generalized Linear Mixed Models: Part II.