Model selection criteria based on cross-validatory concordance statistics

Patrick Ten Eyck () and Joseph E. Cavanaugh
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Patrick Ten Eyck: The University of Iowa
Joseph E. Cavanaugh: The University of Iowa

Computational Statistics, 2018, vol. 33, issue 2, No 2, 595-621

Abstract: Abstract In the logistic regression framework, we present the development and investigation of three model selection criteria based on cross-validatory analogues of the traditional and adjusted c-statistics. These criteria are designed to estimate three corresponding measures of predictive error: the model misspecification prediction error, the fitting sample prediction error, and the sum of prediction errors. We aim to show that these estimators serve as suitable model selection criteria, facilitating the identification of a model that appropriately balances goodness-of-fit and parsimony, while achieving generalizability. We examine the properties of the selection criteria via an extensive simulation study designed as a factorial experiment. We then employ these measures in a practical application based on modeling the occurrence of heart disease.

Keywords: Akaike information criterion; Logistic regression; Prediction; ROC curve; Variable selection (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-017-0766-7

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