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, 54 (3), 921-38

A Semiparametric Bayesian Approach to the Random Effects Model

  • PMID: 9750242

A Semiparametric Bayesian Approach to the Random Effects Model

K P Kleinman et al. Biometrics.


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.

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