On overfitting and post-selection uncertainty assessments. (4th January 2018)

• Record Type: Journal Article
• Authors: Hong, L; Kuffner, T A; Martin, R
• Is Part Of: Biometrika. Volume 105:Number 1(2018:Mar.)
• Page Start: 221
• Page End: 224
• Publication Date: 2018-01-04
• Subjects: Akaike information criterion -- Bayesian information criterion -- Model selection -- Regression; Biometry -- Periodicals; 570.1519505
• DOI: 10.1093/biomet/asx083
• Languages: English
• ISSNs: 0006-3444

Abstract

Summary : In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected submodel, may not be valid because it ignores the selected submodel's dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.