Optimal predictive model selection

• 成果类型: Article
• 署名作者: Barbieri, MM; Berger, JO
• 署名单位: Roma Tre University; Duke University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053604000000238
• 发表日期: 2004
• 页码: 870-897
• 关键词: VARIABLE SELECTION; Bayes Factors

摘要

Often the goal of model selection is to choose a model for future prediction, and it is natural to measure the accuracy of a future prediction by squared error loss. Under the Bayesian approach, it is commonly perceived that the optimal predictive model is the model with highest posterior probability, but this is not necessarily the case. In this paper we show that, for selection among normal linear models, the optimal predictive model is often the median probability model, which is defined as the model consisting of those variables which have overall posterior probability greater than or equal to 1/2 of being in a model. The median probability model often differs from the highest probability model.