Journal article
Model selection criteria based on cross-validatory concordance statistics
Computational Statistics, Vol.33(2), pp.595-621
06/2018
DOI: 10.1007/s00180-017-0766-7
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.
Details
- Title: Subtitle
- Model selection criteria based on cross-validatory concordance statistics
- Creators
- Patrick Ten Eyck - 0000 0004 1936 8294 grid.214572.7 Institute for Clinical and Translational Science The University of Iowa SW44-M GH, 200 Hawkins Dr. Iowa City IA 52242 USAJoseph Cavanaugh - 0000 0004 1936 8294 grid.214572.7 Department of Biostatistics The University of Iowa Iowa City IA USA
- Resource Type
- Journal article
- Publication Details
- Computational Statistics, Vol.33(2), pp.595-621
- DOI
- 10.1007/s00180-017-0766-7
- ISSN
- 0943-4062
- eISSN
- 1613-9658
- Publisher
- Springer Berlin Heidelberg; Berlin/Heidelberg
- Language
- English
- Date published
- 06/2018
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9983985929602771
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