ISBN: 9780387479415

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 … Plus…

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. This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. Buch (fremdspr.) Bücher>Fremdsprachige Bücher>Englische Bücher, Springer Nature<

2007, ISBN: 9780387479415

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 … Plus…

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. Buch (fremdspr.) Jiming Jiang Gebundene Ausgabe, Springer US, 09.03.2007, Springer US, 2007<

2007, ISBN: 9780387479415

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 … Plus…

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. Buch (fremdspr.) Jiming Jiang gebundene Ausgabe, Springer, 09.03.2007, Springer, 2007<

2007, ISBN: 9780387479415

[ED: Gebunden], [PU: Springer US], This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to-date account o… Plus…

[ED: Gebunden], [PU: Springer US], This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. 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 includes 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., DE, [SC: 0.00], Neuware, gewerbliches Angebot, 257, [GW: 505g], 2007 ed., Sofortüberweisung, PayPal, Banküberweisung<

ISBN: 9780387479415

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to-date account of theory and methods in analysis of… Plus…

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. 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 includes 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.; Health, Family & Lifestyle, Springer<