A Nonparametric Approach Using Dirichlet Process for Hierarchical Generalized Linear Mixed Models

10.6339/JDS.2010.08(1).562

Jing Wang

Dirichlet process ； generalized linear mixed model ； Gibbs sampler ； Metropolis-Hastings algorithm

Journal of Data Science

8卷1期（2010 / 01 / 01）

43 - 59

英文

In this paper, we propose a nonparametric approach using the Dirichlet processes (DP) as a class of prior distributions for the distribution G of the random effects in the hierarchical generalized linear mixed model (GLMM). The support of the prior distribution (and the posterior distribution) is large, allowing for a wide range of shapes for G. This provides great flexibility in estimating G and therefore produces a more flexible estimator than does the parametric analysis. We present some computation strategies for posterior computations involved in DP modeling. The proposed method is illustrated with real examples as well as simulations.