References
- Bradley, A. P. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30 (7):1145–59. doi:10.1016/S0031-3203(96)00142-2.
- Clemencon, S., N. Vayatis, and M. Depecker. 2009. AUC optimization and the two-sample problem. In Advances in Neural Information Processing Systems 22:360–68.
- Cortes, C., and M. Mohri. 2003. AUC optimization vs. error rate minimization. Neural information processing systems 15 (NIPS). MIT Press.
- Denis, J., et al. 2010. A program for computing the predication probability and the related receiver operating characteristic graph. Anesthesia & Analgesia 111:1416–21. doi:10.1213/ANE.0b013e3181fb919e.
- Fawcett, T. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27:861–74. doi:10.1016/j.patrec.2005.10.010.
- Finlay, S. 2012. Credit Scoring, Response Modeling, and Insurance Rating: A Practical Guide to Forecasting Consumer Behavior 2nd ed.
- Gönen, M. 2006. Receiver Operating Characteristic (ROC) Curves. Paper 210–31, in: Proceedings of the SUGI 31, San Francisco, USA.
- Hand, D. J., and J. T. Robert. 2001. A simple generalisation of the area under the ROC curve for multiple class classification problems. Machine Learning 45:171–86. doi:10.1023/A:1010920819831.
- Hanley, J. A., and B. J. McNeil. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36. doi:10.1148/radiology.143.1.7063747.
- Kraus, A. 2014. Recent methods from statistics and machine learning for credit scoring. Ph.D. Dissertation. Ph.D. thesis, Ludwig-Maximilians-Universitat Munchen.
- Mason, S. J., and N. E. Graham. 2002. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Quarterly Journal of the Royal Meteorological Society 128:2145–66. doi:10.1256/003590002320603584.
- Narayanan, A., and D. Watts. 1996. Exact methods in the NPAR1WAY procedure. Proceedings of the 21st Annual SUGI Conference, Chicago, Illinois, USA.
- Pandey, M., et al. 2016. ROC Curve: Making way for correct diagnosis. SP11 – PharmaSUG 2016.
- Rasouliyan, L., and D. P. Miller. 2012. The logic and logistics of logistic regression including new features in SAS® 9.2. Budapest, Hungary: The Pharmaceutical Users Software Exchange (PhUSE).
- Refaat, M. 2011. Credit risk scorecards: Development and implementation using SAS. Raleigh, North Carolina, USA: LULU.COM.
- Siddiqi, N. 2006. Credit risk scorecards: Developing and implementing intelligent credit scoring. Hoboken, New Jersey: John Wiley & Sons, Inc.
- Thomas, L. C. 2009. Consumer credit models: Pricing, profit and portfolios. Oxford University Press, 1st Edition.
- Wilcoxon, F. 1945. Individual comparisons by ranking methods. Biometrics Bulletin 1 (6):80–83. doi:10.2307/3001968.
- Wild, C. J., and G. A. F. Seber. 1999. The wilcoxonon rank-sum test. Chapter 10 in chance encounters: A first course in data analysis and inference. New York: Wiley & Sons.
- Zeng, G. 2013. Metric divergence measures and information value in credit scoring. Journal of Mathematics 2013, Article ID 848271. doi:10.1155/2013/848271.
- Zeng, G. 2014a. A rule of thumb for reject inference in credit scoring. Mathematical Finance Letters vol. 2014, article 2, 1–13.
- Zeng, G. 2014b. A necessary condition for a good binning algorithm in credit scoring. Applied Mathematical Sciences 8 (65):3229–42. doi:10.12988/ams.2014.44300.
- Zeng, G. 2015. A unified definition of mutual information with applications in machine learning. Mathematical Problems in Engineering vol. 2015, Article ID 201874. doi:10.1155/2015/201874
- Zeng, G. 2016a. A comparison study of computational methods of Kolmogorov–Smirnov statistic in credit scoring. Communications in Statistics: Simulation and Computation. doi:10.1080/03610918.2016.1249883
- Zeng, G. 2016b. On the existence of maximum likelihood estimates for weighted logistic regression. Communications in Statistics: Simulation and Computation. doi:10.1080/03610926.2016.1260742
- Zeng, G. 2017. Invariant properties of logistic regression model in credit scoring under monotonic transformations. Communications in Statistics: Theory and Methods 46 (17):8791–807. doi:10.1080/03610926.2016.1193200.