1 |
Bamber, D. (1975). The area above the ordinal dominance graph and the area below the receiver operating characteristic graph, Journal of Mathematical Psychology, 12, 387-415.
DOI
|
2 |
Conover, W. J. (1980). Practical Nonparametric Statistics, John Wiley & Sons, New York.
|
3 |
Egan, J. P. (1975). Signal Detection Theory and ROC Analysis, Academic Press, New York.
|
4 |
Engelmann, B., Hayden, E. and Tasche, D. (2003). Testing rating accuracy, Risk, 16, 82-86.
|
5 |
Faraggi, D. and Reiser, B. (2002). Estimation of the area under the ROC curve, Statistics in Medicine, 21, 3093-3106.
DOI
ScienceOn
|
6 |
Fawcett, T. (2003). ROC graphs: Notes and practical considerations for data mining researchers, HP Labs Tech Report HPL-2003-4, Available from: http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf
|
7 |
Gibbons, J. D. (1971). Nonparametric Statistical Inference, McGraw-Hill, New York.
|
8 |
Hanley, J. A. and McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology, 143, 29-36.
DOI
|
9 |
Hong, C. S. (2009). Optimal threshold from ROC and CAP curves, Communications in Statistics- Simulation and Computation, 38, 2060-2072.
DOI
ScienceOn
|
10 |
Hong, C. S. and Cho, M. H. (2015). VUS and HUM represented with Mann-Whitney statistic, Communications for Statistical Applications and Methods, 22, 223-232.
DOI
ScienceOn
|
11 |
Hong, C. S., Joo, J. S. and Choi, J. S. (2010). Optimal thresholds from mixture distributions, The Korean Journal of Applied Statistics, 23, 13-28.
DOI
ScienceOn
|
12 |
Hong, C. S., Jung, E. S. and Jung, D. G. (2013). Standard criterion of VUS for ROC surface, The Korean Journal of Applied Statistics, 26, 977-985.
DOI
ScienceOn
|
13 |
Hong, C. S. and Jung, D. G. (2014). Standard criterion of hypervolume under the ROC manifold, Journal of the Korean Data & Information Science Society, 25, 473-483.
DOI
ScienceOn
|
14 |
Joseph, M. P. (2005). A PD validation framework for Basel II internal ratings-based systems, Available from: http://www.business-school.ed.ac.uk/waf/crcarchive/2005/papers/joseph-maurice.pdf
|
15 |
Mann, H. B. and Whitney, D. R. (1947). On a test whether one of two random variables is stochasti- cally larger than the other, Annals of Mathematical Statistics, 18, 50-60.
DOI
|
16 |
Provost, F. and Fawcett, T. (2001). Robust classification for imprecise environments, Machine Learning, 42, 203-231.
DOI
|
17 |
Randles, R. H. and Wolfe, D. A. (1979). Introduction to the Theory of Nonparametric Statistics, John Wiley & Sons, New York.
|
18 |
Rosset, S. (2004). Model selection via the AUC, In Proceedings of the 21st International Conference of Machine Learning, Banff, Canada.
|
19 |
Sobehart, J. R. and Keenan, S. C. (2001). Measuring default accurately, Risk: Credit Risk Special Report, 14, S31-S33.
|
20 |
Swets, J. A. (1988). Measuring the accuracy of diagnostic systems, Science, 240, 1285-1293.
DOI
|
21 |
Swets, J. A., Dawes, R. M. and Monahan, J. (2000). Better decisions through science, Scientific American, 283, 82-87.
|
22 |
Wilcoxon, F. (1945). Individual comparisons by ranking methods, Biometrics Bulletin, 1, 80-83.
DOI
|
23 |
Wilkie, A. D. (2004). Measures for comparing scoring systems. In L. C. Thomas, D. B. Edelman, and J. N. Crook (Eds.), Readings in Credit Scoring, Oxford University Press, Oxford, 51-62.
|
24 |
Zou, K. H., O′Malley, A. J. and Mauri, L. (2007). Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models, Circulation, 115, 654-657.
DOI
ScienceOn
|