[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CSAM.2015.22.6.625

Dirichlet Process Mixtures of Linear Mixed Regressions

Kyung, Minjung (Department of Statistics, Duksung Women's University)

Publication Information

Communications for Statistical Applications and Methods / v.22, no.6, 2015 , pp. 625-637 More about this Journal

Abstract

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

Keywords

normal mixture; cluster specific random effect; model-based cluster; linear mixed regression; Dirichlet process;

Citations & Related Records

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