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Bivariate ROC Curve and Optimal Classification Function
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 Title & Authors
Bivariate ROC Curve and Optimal Classification Function
Hong, C.S.; Jeong, J.A.;
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We propose some methods to obtain optimal thresholds and classification functions by using various cutoff criterion based on the bivariate ROC curve that represents bivariate cumulative distribution functions. The false positive rate and false negative rate are calculated with these classification functions for bivariate normal distributions.
Credit evaluation;default;classification;cutoff criteria;misclassification rate;
 Cited by
Connell, F. A. and Koepsell, T. D. (1985). Measures of gain in certainty from a diagnostic test, American Journal of Epidemiology, 121, 744-753. crossref(new window)

Hong, C. S. and Joo, J. S. (2010). Optimal thresholds from non-normal mixture, The Korean Journal of Applied Statistics, 23, 943-953. crossref(new window)

Hong, C. S., Kim, G. C. and Jeong, J. A. (2012). Bivariate ROC curve, The Korean Journal of Applied Statistics, 19, 277-286. crossref(new window)

Krzanowski, W. J. and Hand, D. J. (2009). ROC Curves for Continuous Data, Chapman & Hall/CRC, Monographs on Statistics & Applied Probability, 111, Florida.

Lambert, J. and Lipkovich, I. (2008). A macro for getting more out of your ROC curve, SAS Global Forum, 231.

Perkins, N. J. and Schisterman, E. F. (2006). The inconsistency of "Optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve, American Journal of Epidemiology, 163, 670-675. crossref(new window)

Youden, W. J. (1950). Index for rating diagnostic test, Cancer, 3, 32-35. crossref(new window)