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Computational Statistics

, Volume 33, Issue 2, pp 595–621 | Cite as

Model selection criteria based on cross-validatory concordance statistics

  • Patrick Ten Eyck
  • Joseph E. Cavanaugh
Original Paper
  • 132 Downloads

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 

Notes

Acknowledgements

We wish to thank our referees for their valuable feedback, which served to improve the original version of this manuscript.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Institute for Clinical and Translational ScienceThe University of IowaIowa CityUSA
  2. 2.Department of BiostatisticsThe University of IowaIowa CityUSA

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