Dirichlet Process Mixture Models with Shrinkage Prior

2020/10/22 by Ding, Dawei, Karabatsos, George
#Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2010.11385

Abstract

We propose Dirichlet Process Mixture (DPM) models for prediction and cluster-wise variable selection, based on two choices of shrinkage baseline prior distributions for the linear regression coefficients, namely the Horseshoe prior and Normal-Gamma prior. We show in a simulation study that each of the two proposed DPM models tend to outperform the standard DPM model based on the non-shrinkage normal prior, in terms of predictive, variable selection, and clustering accuracy. This is especially true for the Horseshoe model, and when the number of covariates exceeds the within-cluster sample size. A real data set is analyzed to illustrate the proposed modeling methodology, where both proposed DPM models again attained better predictive accuracy.

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