Dirichlet process mixture models with shrinkage prior

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