Restricted maximum likelihood and inference of random effects in linear mixed models
Abstract
Chapter 4 concerns the restricted maximum likelihood (REML) estimator and some other Bayes-type techniques applied in longitudinal data analysis. Therefore, Bayes’ theorem and Bayesian inference are reviewed first to familiarize the reader with the rationale of various Bayes-type models and methods included in the current and many of the succeeding chapters. Next, the general specifications and inference of the REML estimator are delineated. Two computational procedures are then displayed for the estimation of model parameters described in Chapter 3: the Newton–Raphson (NR) and the Expectation–Maximization (EM) algorithms. The best linear unbiased ...
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