Resampling-based information criteria for best-subset regression

Philip Reiss (), Lei Huang, Joseph Cavanaugh and Amy Roy

Annals of the Institute of Statistical Mathematics, 2012, vol. 64, issue 6, 1186 pages

Abstract: When a linear model is chosen by searching for the best subset among a set of candidate predictors, a fixed penalty such as that imposed by the Akaike information criterion may penalize model complexity inadequately, leading to biased model selection. We study resampling-based information criteria that aim to overcome this problem through improved estimation of the effective model dimension. The first proposed approach builds upon previous work on bootstrap-based model selection. We then propose a more novel approach based on cross-validation. Simulations and analyses of a functional neuroimaging data set illustrate the strong performance of our resampling-based methods, which are implemented in a new R package. Copyright The Institute of Statistical Mathematics, Tokyo 2012

Keywords: Adaptive model selection; Covariance inflation criterion; Cross-validation; Extended information criterion; Functional connectivity; Overoptimism (search for similar items in EconPapers)
Date: 2012
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DOI: 10.1007/s10463-012-0353-1

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