Linear and Generalized Linear Mixed Models and Their Applications
Over the past decade there has been an explosion of developments in mixed effects models and their applications. This book concentrates on two major classes of mixed effects models, linear mixed models and generalized linear mixed models, with the intention of offering an up-to-date account of theory and methods in the analysis of these models as well as their applications in various fields. The first 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 confidence intervals. On the other hand, the literature on non-Gaussian linear mixed models is much less extensive, partially because of the difficulties 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 offers 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.
1100023628
Linear and Generalized Linear Mixed Models and Their Applications
Over the past decade there has been an explosion of developments in mixed effects models and their applications. This book concentrates on two major classes of mixed effects models, linear mixed models and generalized linear mixed models, with the intention of offering an up-to-date account of theory and methods in the analysis of these models as well as their applications in various fields. The first 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 confidence intervals. On the other hand, the literature on non-Gaussian linear mixed models is much less extensive, partially because of the difficulties 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 offers 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.
139.99
Out Of Stock
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Linear and Generalized Linear Mixed Models and Their Applications
257
Linear and Generalized Linear Mixed Models and Their Applications
257
139.99
Out Of Stock
Product Details
| ISBN-13: | 9780387479415 |
|---|---|
| Publisher: | Springer New York |
| Publication date: | 03/09/2007 |
| Series: | Springer Series in Statistics |
| Edition description: | 2007 |
| Pages: | 257 |
| Product dimensions: | 6.10(w) x 9.25(h) x 0.03(d) |
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