Titlebook: Linear and Generalized Linear Mixed Models and Their Applications; Jiming Jiang Book 20071st edition Springer-Verlag New York 2007 Regress

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Generalized Linear Mixed Models: Part I,es through a link function; and (iii)′ the variance of the observation is a function of the mean. Note that (iii)′ is a result of (ii)′. See McCullagh and Nelder (1989) for details. Unlike linear models, GLMs include a variety of models that includes normal, binomial, Poisson, and multinomial as spe

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Book 20071st editionractice, 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 con

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Linear Mixed Models: Part I,atter can be expressed as . = . + ., where . is a vector of observations, . is a matrix of known covariates, . is a vector of unknown regression coefficients, and . is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases

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Linear Mixed Models: Part II,ce, namely, tests in linear mixed models. Section 2.1.1 discusses statistical tests in Gaussian mixed models. As shown, exact .-tests can often be derived under Gaussian ANOVA models. Furthermore, in some special cases, optimal tests such as uniformly most powerful unbiased (UMPU) tests exist and co

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Generalized Linear Mixed Models: Part I,the observations are discrete, or categorical. For example, the number of heart attacks of a potential patient during the past year takes the values 0, 1, 2, ..., and therefore is a discrete random variable. McCullagh and Nelder (1989) proposed an extension of linear models, called generalized linea

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Generalized Linear Mixed Models: Part II,t to evaluate. For relatively simple models, the likelihood function may be evaluated by numerical integration techniques. See, for example, Hinde (1982), and Crouch and Spiegelman (1990). Such a technique is tractable if the integrals involved are low-dimensional. The following is an example.

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