Jiming Jiang

Linear and Generalized Linear Mixed Models and Their Applications (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 257
• Erscheinungstermin: 30. Mai 2007
• Englisch
• ISBN-13: 9780387479460
• Artikelnr.: 37287023

Autorenporträt

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).

Inhaltsangabe

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.