EXTENDED BIC FOR SMALL-n-LARGE-P SPARSE GLM

被引：143

作者：

Chen, Jiahua ^{[1
]}

Chen, Zehua ^{[2
]}

机构：

[1] Univ British Columbia, Dept Stat, Vancouver, BC V6T 1Z4, Canada

[2] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117543, Singapore

来源：

STATISTICA SINICA | 2012年 / 22卷 / 02期

关键词：

Consistency; exponential family; extended Bayes information criterion; feature selection; generalized linear model; small-n-large-P; VARIABLE SELECTION; MODEL SELECTION; MULTIPLE; REGULARIZATION; CRITERION; LOCI;

D O I：

10.5705/ss.2010.216

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The small-n-large-P situation has become common in genetics research, medical studies, risk management, and other fields. Feature selection is crucial in these studies yet poses a serious challenge. The traditional criteria such as AIC, BIC, and cross-validation choose too many features. In this paper, we examine the variable selection problem under the generalized linear models. We study the approach where a prior takes specific account of the small-n-large-P situation. The criterion is shown to be variable selection consistent under generalized linear models. We also report simulation results and a data analysis to illustrate the effectiveness of EBIC for feature selection.

引用

页码：555 / 574

页数：20

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