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Extended BIC for small-\(n\)-large-\(P\) sparse GLM. (English) Zbl 1238.62080
Summary: 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. 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.

MSC:
62J12 Generalized linear models (logistic models)
62F12 Asymptotic properties of parametric estimators
65C60 Computational problems in statistics (MSC2010)
62F07 Statistical ranking and selection procedures
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