Average Reviews:
(More customer reviews)This is a very recent and authoritative treatment of classical parametric models, starting with the general linear model and extending to generalized linear models, linear mixed models and finally to generalized linear mixed models. It also has applciations to longitudinal data analysis and prediction problems. Heavy on theory and matrix algebra but not loaded with applications. Good for a graduate course in statistics especially for Ph.D. students. It is concise covering a large range of topics in only 310 pages. An interesting feature is a chapter on computing that deals with Markov chain Monte Carlo methods in some detail.
There is also a brief chapter on nonlinear models (only 5 pages) that includes an example of corn photosynthesis and also the important application to pharmacokinetic models.
The emphasis is on maximum likelihood estimation and its extensions (e.g. restricted maximum likelihood and penalized likelihood and quasi-likelihood). The authors provide an interesting perspective on the non-applicability of analysis of variance techniques in some mixed effects models.
Comment | Permalink
Click Here to see more reviews about: Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics)
An accessible and self-contained introduction to statistical models-now in a modernized new editionGeneralized, Linear, and Mixed Models, Second Edition provides an up-to-date treatment of the essential techniques for developing and applying a wide variety of statistical models. The book presents thorough and unified coverage of the theory behind generalized, linear, and mixed models and highlights their similarities and differences in various construction, application, and computational aspects.A clear introduction to the basic ideas of fixed effects models, random effects models, and mixed models is maintained throughout, and each chapter illustrates how these models are applicable in a wide array of contexts. In addition, a discussion of general methods for the analysis of such models is presented with an emphasis on the method of maximum likelihood for the estimation of parameters. The authors also provide comprehensive coverage of the latest statistical models for correlated, non-normally distributed data. Thoroughly updated to reflect the latest developments in the field, the Second Edition features:
A new chapter that covers omitted covariates, incorrect random effects distribution, correlation of covariates and random effects, and robust variance estimation
A new chapter that treats shared random effects models, latent class models, and properties of models
A revised chapter on longitudinal data, which now includes a discussion of generalized linear models, modern advances in longitudinal data analysis, and the use between and within covariate decompositions
Expanded coverage of marginal versus conditional models
Numerous new and updated examples
With its accessible style and wealth of illustrative exercises, Generalized, Linear, and Mixed Models, Second Edition is an ideal book for courses on generalized linear and mixed models at the upper-undergraduate and beginning-graduate levels. It also serves as a valuable reference for applied statisticians, industrial practitioners, and researchers.
Buy cheap Generalized, Linear, and Mixed Models (Wiley Series in Probability and Statistics) now.
No comments:
Post a Comment