A Semiparametric Bayesian Approach to the Random Effects Model
KEN P. KLEINMAN, Joseph G. Ibrahim · Biometrics · 1998
In longitudinal random effects models, the random effects are typically assumed to have a normal distribution in both Bayesian and classical models. We provide a Bayesian model that allows the random effects to have a nonparametric prior distribution. We propose a Dirichlet process prior for the distribution of the random effects; computation is made possible by the Gibbs sampler. An example using marker data from an AIDS study is given to illustrate the methodology.