Covariate-Adjusted Constrained Bayes Predictions of Random Intercepts and Slopes

Constrained Bayes methodology represents an alternative to the posterior mean ( empirical Bayes) method commonly used to produce random effect predictions under mixed linear models. The general constrained Bayes methodology of Ghosh (1992) is compared to a direct implementation of constraints, and it is suggested that the former approach could feasibly be incorporated into commercial mixed model software. Simulation studies and a real-data example illustrate the main points and support the conclusions.