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http://dx.doi.org/10.5351/CKSS.2012.19.2.277

Bivariate ROC Curve  

Hong, C.S. (Department of Statistics, Sungkyunkwan University)
Kim, G.C. (Research Institute of Applied Statistics, Sungkyunkwan University)
Jeong, J.A. (Research Institute of Applied Statistics, Sungkyunkwan University)
Publication Information
Communications for Statistical Applications and Methods / v.19, no.2, 2012 , pp. 277-286 More about this Journal
Abstract
For credit assessment models, the ROC curves evaluate the classification performance using two univariate cumulative distribution functions of the false positive rate and true positive rate. In this paper, it is extended to two bivariate normal distribution functions of default and non-default borrowers; in addition, the bivariate ROC curves are proposed to represent the joint cumulative distribution functions by making use of the linear function that passes though the mean vectors of two score random variables. We explore the classification performance based on these ROC curves obtained from various bivariate normal distributions, and analyze with the corresponding AUROC. The optimal threshold could be derived from the bivariate ROC curve using many well known classification criteria and it is possible to establish an optimal cut-off criteria of bivariate mixture distribution functions.
Keywords
Classification performance; credit evaluation; default; optimal threshold; score;
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Times Cited By KSCI : 2  (Citation Analysis)
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