[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CSAM.2017.24.5.457

Bayesian analysis of random partition models with Laplace distribution

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

Publication Information

Communications for Statistical Applications and Methods / v.24, no.5, 2017 , pp. 457-480 More about this Journal

Abstract

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

Keywords

Laplace mixture; model-based cluster; random partition model; Dirichlet process prior;

Citations & Related Records

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