Variable Selection Via Gibbs Sampling
提出一种基于吉布斯采样的变量选择方法，通过将回归模型嵌入层次正态混合模型，利用后验概率识别有潜力的预测变量子集，适用于多元回归中的变量筛选。
Abstract A crucial problem in building a multiple regression model is the selection of predictors to include. The main thrust of this article is to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets. This procedure entails embedding the regression setup in a hierarchical normal mixture model where latent variables are used to identify subset choices. In this framework the promising subsets of predictors can be identified as those with higher posterior probability. The computational burden is then alleviated by using the Gibbs sampler to indirectly sample from this multinomial posterior distribution on the set of possible subset choices. Those subsets with higher probability—the promising ones—can then be identified by their more frequent appearance in the Gibbs sample.