MODEL SELECTION FOR MULTIVARIATE REGRESSION IN SMALL SAMPLES

被引：155

作者：

BEDRICK, EJ ^{[1
]}

TSAI, CL ^{[1
]}

机构：

[1] UNIV CALIF DAVIS,GRAD SCH MANAGEMENT,DAVIS,CA 95616

来源：

BIOMETRICS | 1994年 / 50卷 / 01期

关键词：

AIC; AIC(C); ICOMP; KULLBACK-LEIBLER INFORMATION; MALLOWS C-P;

D O I：

10.2307/2533213

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

We develop a small-sample criterion (AIC(C)) for selecting multivariate regression models. This criterion adjusts the Akaike information criterion to be an exact unbiased estimator for the expected Kullback-Leibler information. A small-sample comparison shows that AIC(C) provides better model order choices than other available model selection methods. Data from an agricultural experiment are analyzed.

引用

页码：226 / 231

页数：6

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