Titlebook: Linear and Generalized Linear Mixed Models and Their Applications; Jiming Jiang,Thuan Nguyen Book 2021Latest edition Springer Science+Busi

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Springer Series in Statisticshttp://image.papertrans.cn/l/image/586453.jpg

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https://doi.org/10.1007/978-1-0716-1282-8Regression analysis; data analysis; generalized linear mixed models; linear mixed models; linear optimiz

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978-1-0716-1284-2Springer Science+Business Media, LLC, part of Springer Nature 2021

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Generalized Linear Mixed Models: Part I,tions are discrete, or categorical. For example, the number of seizure attacks of a potential patient during the past year takes the values 0, 1, 2, …and therefore is a discrete random variable; employment status (e.g., full-time/part-time/retired/unemployed) of a sampled individual is a categorical random variable.

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0172-7397 e analysis of these models as well as their applications in .Now in its second edition, 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 a

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Book 2021Latest editionpresents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorpora

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Linear Mixed Models: Part I,icients, and . is a vector of (unobservable random) errors. In this model, the regression coefficients are considered as fixed, unknown constants. However, there are cases in which it makes sense to assume that some of these coefficients are random.

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Linear Mixed Models: Part II,ived under Gaussian ANOVA models. Furthermore, in some special cases, optimal tests such as uniformly most powerful unbiased (UMPU) tests exist and coincide with the exact .-tests. Section 2.1.2 considers tests in non-Gaussian linear mixed models.