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Biometrics. 1998 Sep;54(3):921-38.

A semiparametric Bayesian approach to the random effects model.

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Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.


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.

[Indexed for MEDLINE]

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