Generalized linear mixed models on nonlinear longitudinal data
Abstract
Chapter 8 starts the description of models and methods for the analysis of non-normal longitudinal data. A brief overview is provided first on the basic specifications of generalized linear models (GLMs), based on which statistical inference of generalized linear mixed models (GLMMs) is introduced. Next, I display five approximation methods for the estimation of the fixed and the random effects in GLMMs: the penalized quasi-likelihood (PQL) method, the marginal quasi-likelihood (MQL) technique, the Laplace approximation, Gaussian quadrature rules, and the Markov chain Monte Carlo approach. The merits and limitations in these approximation methods are discussed with ...
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