Generalized Linear Mixed ModelsIMS, 2003 - 84 pages Wiley Series in Probability and Statistics A modern perspective on mixed models The availability of powerful computing methods in recent decades has thrust linear and nonlinear mixed models into the mainstream of statistical application. This volume offers a modern perspective on generalized, linear, and mixed models, presenting a unified and accessible treatment of the newest statistical methods for analyzing correlated, nonnormally distributed data. As a follow-up to Searle's classic, Linear Models, and Variance Components by Searle, Casella, and McCulloch, this new work progresses from the basic one-way classification to generalized linear mixed models. A variety of statistical methods are explained and illustrated, with an emphasis on maximum likelihood and restricted maximum likelihood. An invaluable resource for applied statisticians and industrial practitioners, as well as students interested in the latest results, Generalized, Linear, and Mixed Models features: * A review of the basics of linear models and linear mixed models * Descriptions of models for nonnormal data, including generalized linear and nonlinear models * Analysis and illustration of techniques for a variety of real data sets * Information on the accommodation of longitudinal data using these models * Coverage of the prediction of realized values of random effects * A discussion of the impact of computing issues on mixed models |
Autres éditions - Tout afficher
Generalized, Linear, and Mixed Models Charles E. McCulloch,Shayle R. Searle Affichage d'extraits - 2001 |
Generalized, Linear, and Mixed Models Charles E. McCulloch,Shayle R. Searle Aucun aperçu disponible - 2004 |
Expressions et termes fréquents
algorithm Amer approach approximation Assoc assumed assumption Bayesian best predicted values beta-binomial model binary data Biometrika blight example BLUP calculate Chapter chestnut blight computational conditional distribution conditional likelihood consider correlation covariance donor estimating equations evaluation expected value exponential family Əln fixed effects GEES genes genetic given gives GLMMs Heagerty hypothesis indep inference inferential goals integral intercept Io² isolates iterated Laplace approximation likelihood ratio test linear mixed models linear models linear predictor link function log likelihood logit marginal mean marginal model maximize maximum likelihood McCulloch method ML estimates Monte Carlo Nelder normal distribution NSF-CBMS OLSE p-value Poisson probit model Progabide Propranolol quasi-likelihood random effects distribution random effects models random factors regression REML sample saturated model Searle situation specify ẞ3SUNit ẞxij standard errors Statist transmission treatment group variance components variance-covariance matrix vector virus Xẞ Yijk Zeger zero
Fréquemment cités
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Page 2 - For each combination of isolates they have averaged about 30 attempts and record a binary response of whether or not the attempt succeeded in transmitting the virus. Questions of interest include whether pre-identified genes actually do have an influence on transmission of the virus (and if so, to what degree), whether there are other, as yet unidentified, genes which might affect transmission, and whether transmission is symmetric.
Page 1 - Viruses spread between fungal individuals when they come in contact and fuse together. A major obstacle in spreading this virus and thus controlling the disease is that different isolates of the fungus cannot necessarily transfer the virus to one another.
Page 11 - ... have been selected. • Incorporation of correlation in the model. Observations that share the same level of the random effect are being modeled as correlated. • Accuracy of estimates. Using random factors involves making extra assumptions but gives more accurate estimates. • Estimation...