Linear and Generalized Linear Mixed Models and Their Applications

Jiming Jiang

ISBN 10: 0387479414 / ISBN 13: 9780387479415

Published by Springer, Berlin, Springer New York, Springer, 2007

About this Item

Neuware - 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.

Seller Inventory # 9780387479415

Bibliographic Details

• Title: Linear and Generalized Linear Mixed Models and Their Applications
• Publisher: Springer, Berlin, Springer New York, Springer
• Publication Date: 2007
• Language: English
• ISBN 10: 0387479414
• ISBN 13: 9780387479415
• Binding: Buch
• Condition: Neu
• Dimensions: 242W x 156H x 21D millimeters 242 millimeters width by 156 millimeters height by 21 millimeters depth
• Weight: 579 grams

About this title

Synopsis

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.

About the Author

Jiming Jiang is Professor of Statistics and Director of the Statistical Laboratory at UC-Davis.