论文标题
Dirichlet工艺混合模型与先验的收缩
Dirichlet Process Mixture Models with Shrinkage Prior
论文作者
论文摘要
我们提出了Dirichlet工艺混合物（DPM）模型，以进行预测和群集变量选择，这是基于线性回归系数的收缩基线先验分布的两种选择，即马蹄前和正常γ先验。我们在一项模拟研究中表明，在预测性，可变选择和聚类准确性方面，两个提出的DPM模型中的每一个倾向于优于标准DPM模型。对于马蹄模型，当协变量超过集群内样本量时，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，在该方法中，两种建议的DPM模型再次获得了更好的预测精度。
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.
