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Linear and Generalized Linear Mixed Models and Their Applications

Produktform: E-Buch Text Elektronisches Buch in proprietärem

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.weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Elektronisches Format: PDF

Sprache(n): Englisch

ISBN: 978-1-07-161282-8 / 978-1071612828 / 9781071612828

Verlag: Springer US

Erscheinungsdatum: 22.03.2021

Seiten: 343

Auflage: 2

Autor(en): Thuan Nguyen, Jiming Jiang

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