THE RISK INFLATION CRITERION FOR MULTIPLE-REGRESSION

• 成果类型: Article
• 署名作者: FOSTER, DP; GEORGE, EI
• 署名单位: University of Texas System; University of Texas Austin
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176325766
• 发表日期: 1994
• 页码: 1947-1975
• 关键词: selection; variables; MODEL; prediction

摘要

A new criterion is proposed for the evaluation of variable selection procedures in multiple regression. This criterion, which we call the risk inflation, is based on an adjustment to the risk. Essentially, the risk inflation is the maximum increase in risk due to selecting rather than knowing the ''correct'' predictors. A new variable selection procedure is obtained which, in the case of orthogonal predictors, substantially improves on AIC, C-p and BIC and is close to optimal. In contrast to AIC, C-p and BIC which use dimensionality penalties of 2, 2 and log n, respectively, this new procedure uses a penalty 2 log p, where p is the number of available predictors. For the case of nonorthogonal predictors, bounds for the optimal penalty are obtained.