Dirichlet Process Mixtures of Block g Priors for Model Selection and Prediction in Linear Models
线性模型选择与预测中的分块 g 先验狄利克雷过程混合
This paper introduces Dirichlet process mixtures of block g priors for model selection and prediction in linear models. These priors are extensions of traditional mixtures of g priors that allow for differential shrinkage for various (data-selected) blocks of parameters while fully accounting for the predictors’ correlation structure, providing a bridge between the literatures on model selection and continuous shrinkage priors. We show that Dirichlet process mixtures of…