Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for ...

Abstract We give a short but detailed review of the methods used to deal with linear mixed models (restricted likelihood, AIREML algorithm, best linear unbiased predictors, etc.), with a few original ...

Scientists may wish to analyze correlated outcome data with constraints among the responses. For example, piecewise linear regression in a longitudinal data analysis can require use of a general ...

A few other procedures in SAS/STAT software offer limited mixed-linear-model capabilities. PROC GLM fits some random-effects and repeated-measures models, although its methods are based on ...