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 the Normal‐Gamma prior. We show in a simulation study that each of the two proposed DPM models tends 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 analysed to illustrate the proposed modelling methodology, where both proposed DPM models again attained better predictive accuracy.

中文翻译：

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预先收缩的Dirichlet工艺混合物模型

我们基于线性回归系数的收缩基线先验分布的两种选择，即马蹄先验和正态伽马先验，提出了用于预测和聚类变量选择的Dirichlet过程混合（DPM）模型。我们在模拟研究中表明，在预测，变量选择和聚类准确性方面，基于非收缩正常先验，两个提出的DPM模型都倾向于优于标准DPM模型。对于马蹄模型，以及当协变量的数量超过簇内样本大小时，尤其如此。分析了一个真实的数据集以说明所提出的建模方法，其中所提出的两个DPM模型都再次获得了更好的预测精度。