DOI QR코드

DOI QR Code

Estimating Discriminatory Power with Non-normality and a Small Number of Defaults

  • Hong, C.S. ;
  • Kim, H.J. ;
  • Lee, J.L.
  • Received : 2012.09.04
  • Accepted : 2012.10.08
  • Published : 2012.10.31

Abstract

For credit evaluation models, we extend the study of discriminatory power based on AUC obtained from a ROC curve when the number of defaults is small and distribution functions of the defaults and non-defaults are normal distributions. Since distribution functions do not satisfy normality in real world, the distribution functions of the defaults and non-defaults are assumed as normal mixture distributions based on results that the normal mixture could be better fitted than other distribution estimation methods for non-normal data. By using several AUC statistics, the discriminatory power under such a circumstance is explored and compared with those of normal distributions.

Keywords

AUC;bootstrap;credit evaluation;kernel density;ROC

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