AN OPTIMAL SELECTION OF REGRESSION VARIABLES
成果类型：
Article
署名作者：
SHIBATA, R
署名单位：
Institute of Science Tokyo; Tokyo Institute of Technology
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
发表日期：
1981
页码：
4554
关键词：
摘要：
An asymptotically optimal selection of regression variables is proposed. The key assumption is that the number of control variables is infinite or increases with the sample size. Mallows'' Cp, Akaike''s FPE and AIC methods are all asymptotically equivalent to this method.