Dirichlet Process Mixture Models with Shrinkage Prior

Dawei Ding; George Karabatsos

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arXiv · 2010.11385

Dirichlet Process Mixture Models with Shrinkage Prior

Dawei Ding·George Karabatsos

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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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Dawei Ding, George Karabatsos. 2020-10-22. Dirichlet Process Mixture Models with Shrinkage Prior. https://arxiv.org/abs/2010.11385

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