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Alternative Title
Abstract
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.
Keywords
Consistency, Exponential family, Extended Bayes information criterion, Feature selection, Generalized linear model, Small-n-large-P
Source Title
Statistica Sinica
Publisher
Series/Report No.
Collections
Rights
Date
2012-04
DOI
10.5705/ss.2010.216
Type
Article