- Volume 25 Issue 2
In constructing a credit scorecard, each characteristic variable is divided into a few attributes; subsequently, weights are assigned to those attributes in a process called coarse classification. While partitioning a characteristic variable into attributes, one should determine appropriate cutpoints for the partition. In this paper, we propose a cutpoint selection method via penalization. In addition, we compare the performances of the proposed method with classification spline machine (Koo et al., 2009) on both simulated and real credit data.
Classification spline machine;coarse classification;credit scorecard
- 구자용, 최대우, 최민성 (2005). 스플라인을 이용한 신용평점화, <응용통계연구>, 1, 543-553.
- 하재환, 박창이 (2009). 선형판별분석에서의 변수 선택, Journal of the Korean Data Analysis Society, 11, 381-389.
- Breiman, L. (1996). Heuristics of instability and stabilization in model selection, Annals of Statistics, 24, 2350-2383. https://doi.org/10.1214/aos/1032181158
- Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of the American Statistical Association, 96, 1348-1360. https://doi.org/10.1198/016214501753382273
- Hand, D. J. and Adams, N. M. (2000). Defining attributes for scorecard construction in credit scoring, Journal of Applied Statistics, 27, 527-540. https://doi.org/10.1080/02664760050076371
- Hand, D. J. and Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review, Journal of the Royal Statistical Society Series A, 160, 523-541. https://doi.org/10.1111/j.1467-985X.1997.00078.x
- Koo, J.-Y., Park, C. and Jhun, M. (2009). A classication spline machine for building a credit scorecard, Journal of Statistical Computation and Simulation, 79, 681-689. https://doi.org/10.1080/00949650701859577
- Kooperberg, C., Bose, S. and Stone, C. J. (1997). Polychotomous regression, Journal of the American Statistical Association, 92, 117-127. https://doi.org/10.1080/01621459.1997.10473608
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society Series B, 58, 267-288.
- Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables, Journal of Royal Statistical Society Series B, 68, 49-67. https://doi.org/10.1111/j.1467-9868.2005.00532.x
- Zou, H. (2006). The adaptive lasso and its oracle properties, Journal of the American Statistical Association, 101, 1418-1429. https://doi.org/10.1198/016214506000000735
- Categorical Variable Selection in Naïve Bayes Classification vol.28, pp.3, 2015, https://doi.org/10.5351/KJAS.2015.28.3.407
- Fused least absolute shrinkage and selection operator for credit scoring vol.85, pp.11, 2015, https://doi.org/10.1080/00949655.2014.922685
- Developing the high risk group predictive model for student direct loan default using data mining vol.26, pp.6, 2015, https://doi.org/10.7465/jkdi.2015.26.6.1417
- Developing the credit risk scoring model for overdue student direct loan vol.27, pp.5, 2016, https://doi.org/10.7465/jkdi.2016.27.5.1293
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