zbMATH — the first resource for mathematics

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.

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