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Consistency of AUC Maximization as an Estimator of Binary Choice Models

Fedotenkov I. Consistency of the estimator of binary response models based on AUC maximization. Statistical Methods & Applications 22(3), Springer, 2013, p. 381-390.

10 Pages Posted: 25 Jan 2012 Last revised: 27 Aug 2015

Igor Fedotenkov

affiliation not provided to SSRN

Date Written: January 24, 2012

Abstract

This paper studies the asymptotic properties of the binary choice model estimator, based on the maximization of the Area Under receiver operating characteristic Curve (AUC). It is shown that under certain assumptions AUC maximization is a consistent method of binary choice models estimation up to normalizations. As AUC is equivalent to Mann-Whitney U statistics and Wilcoxon test of ranks, maximization of area under ROC curve is equivalent to the maximization of corresponding statistics. Comparing with the parametric methods, such as logit and probit, AUC maximization relaxes assumptions about the distribution of errors, but imposes some restrictions on the distribution of regressors, which can be easily checked, since this information is observed.

Keywords: ROC, AUC maximization, consistency, binary choice model

JEL Classification: C01, C13

Suggested Citation

Fedotenkov, Igor, Consistency of AUC Maximization as an Estimator of Binary Choice Models (January 24, 2012). Fedotenkov I. Consistency of the estimator of binary response models based on AUC maximization. Statistical Methods & Applications 22(3), Springer, 2013, p. 381-390.. Available at SSRN: https://ssrn.com/abstract=1991051 or http://dx.doi.org/10.2139/ssrn.1991051

Igor Fedotenkov (Contact Author)

affiliation not provided to SSRN

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