Rao–Blackwellization for Bayesian Variable Selection and Model Averaging in Linear and Binary Regression: A Novel Data Augmentation Approach
线性与二元回归中贝叶斯变量选择和模型平均的 Rao–Blackwell 化
Choosing the subset of covariates to use in regression or generalized linear models is a ubiquitous problem. The Bayesian paradigm addresses the problem of model uncertainty by considering models corresponding to all possible subsets of the covariates, where the posterior distribution over models is used to select models or combine them via Bayesian model averaging (BMA). Although conceptually straightforward, BMA is often difficult to implement in practice, since either the…