title: Covariate-Adjusted Constrained Bayes Predictions of Random Intercepts and Slopes.
abstract: 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.
authors.affiliation: Department of Biostatistics at The Rollins School of Public Health. rlyles@emory.edu .
authors.forename: Robert H
authors.initials: RH
authors.lastname: Lyles
authors.affiliation: Department of Biostatistics and Epidemiology in the School of Medicine. rhmoore@mail.med.upenn.edu .
authors.forename: Reneé H
authors.initials: RH
authors.lastname: Moore
authors.affiliation: Department of Biostatistics at The Rollins School of Public Health. amanatu@emory.edu .
authors.forename: Amita K
authors.initials: AK
authors.lastname: Manatunga
authors.affiliation: Department of Biostatistics at The Rollins School of Public Health. keasle2@emory.edu .
authors.forename: Kirk A
authors.initials: KA
authors.lastname: Easley
keywords: Mixed linear model; prediction; random effects; shrinkage