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http://dx.doi.org/10.5351/KJAS.2012.25.5.803

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

Hong, C.S. (Department of Statistics, Sungkyunkwan University)
Kim, H.J. (Research Institute of Applied Statistics, Sungkyunkwan University)
Lee, J.L. (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.25, no.5, 2012 , pp. 803-811 More about this Journal
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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