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Resampling-based information criteria for best-subset regression

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  • Philip Reiss
  • Lei Huang
  • Joseph Cavanaugh
  • Amy Roy

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

Suggested Citation

  • Philip Reiss & Lei Huang & Joseph Cavanaugh & Amy Roy, 2012. "Resampling-based information criteria for best-subset regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(6), pages 1161-1186, December.
    • Handle: RePEc:spr:aistmt:v:64:y:2012:i:6:p:1161-1186
    DOI: 10.1007/s10463-012-0353-1
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    References listed on IDEAS

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