Resampling-based information criteria for best-subset regression |
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Authors: | Philip T Reiss Lei Huang Joseph E Cavanaugh Amy Krain Roy |
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Institution: | (1) Department of Statistics, Texas A&M University, College Station, TX, USA |
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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. |
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