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Linear and Generalized Linear Mixed Models and Their Applications by Jiming Jiang download in iPad, ePub, pdf

At the same

This volume is intended to provide an up-to-date treatment of both theory and methods. At the same time, they formulate computationally less complex alternatives, including generalized estimating equations and pseudo-likelihood methods. The authors begin with models for the full marginal distribution of the outcome vector.

They then briefly introduce conditional models and move on to the random-effects family, encompassing the beta-binomial model, the probit model and, in particular the generalized linear mixed model. The authors critique frequently used methods and propose flexible and broadly valid methods instead, and conclude with key concepts of sensitivity analysis. Course Description This course will focus on the application of generalized linear and generalized linear mixed models for medical applications with a binary or count outcome. The course will give an introduction to the generalized linear model and extend it generalized linear mixed models to cope with potentially nested fixed and random effects simultaneously. The book has a nice lay-out and the index make it easy to jump to a topic of interest.

This volume is intended to provide

They sketch the general principles for how to deal with the commonly encountered issue of incomplete longitudinal data. However, these assumptions are inappropriate for some types of response variables. The examples of applications appear near the end of each chapter. The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis.

The material is complete enough to cover a course in a Ph. This allows model fitting to be based on maximum likelihood principles, immediately implying inferential tools for all parameters in the models. Participants are expected to have a good working knowledge of simple statistical methods, including a basic understanding of regression and analysis of variance.

Participants may be academic researchers in the Medical and Health or Social Sciences sector or may work within the Government, pharmaceutical industry, or other parts of the private sector. Imagine, for example, a model that predicts the likelihood of a given person going to the beach as a function of temperature. This book contains many examples, exercises and some useful appendices, making it suitable for use in statistics courses.