응용통계연구 (The Korean Journal of Applied Statistics)

  • 제25권2호
  • /
  • Pages.261-267
  • /
  • 2012
  • /
  • 1225-066X(pISSN)
  • /
  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
권호 표지
DOI QR코드

DOI QR Code

신용평점화에서 벌점화를 이용한 절단값 선택

Cutpoint Selection via Penalization in Credit Scoring

  • 진슬기 (서울시립대학교 통계학과) ;
  • 김광래 (고려대학교 통계연구소) ;
  • 박창이 (서울시립대학교 통계학과)
  • 투고 : 2011.10.14
  • 심사 : 2011.12.26
  • 발행 : 2012.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

신용평점표(credit scorecard) 작성시 각 특성변수(characteristic variable)들을 몇 개의 속성(attribute)들로 나누고 각 속성에 적절한 가중치를 부여하게 된다. 이 과정을 성김화(coarse classi cation)라 한다. 특성변수들을 속성들로 나눌 때 그 기준이 되는 절단값(cutpoint)을 선택해야 한다. 본 논문에서는 벌점화(penalization) 기반의 절단값 선택법을 제안한다. 또한 여러가지 모의실험과 실제 신용자료의 분석을 통하여 제안된 방법과 기존의 절단값 선택법인 스플라인 분류 기계 (Koo 등, 2009)의 성능을 비교한다.

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.

키워드

참고문헌

  1. 구자용, 최대우, 최민성 (2005). 스플라인을 이용한 신용평점화, <응용통계연구>, 1, 543-553.
  2. 하재환, 박창이 (2009). 선형판별분석에서의 변수 선택, Journal of the Korean Data Analysis Society, 11, 381-389.
  3. Breiman, L. (1996). Heuristics of instability and stabilization in model selection, Annals of Statistics, 24, 2350-2383. https://doi.org/10.1214/aos/1032181158
  4. 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
  5. 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
  6. 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
  7. Koo, J.-Y., Park, C. and Jhun, M. (2009). A classi cation spline machine for building a credit scorecard, Journal of Statistical Computation and Simulation, 79, 681-689. https://doi.org/10.1080/00949650701859577
  8. 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
  9. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society Series B, 58, 267-288.
  10. 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
  11. 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

피인용 문헌

  1. Categorical Variable Selection in Naïve Bayes Classification vol.28, pp.3, 2015, https://doi.org/10.5351/KJAS.2015.28.3.407
  2. Fused least absolute shrinkage and selection operator for credit scoring vol.85, pp.11, 2015, https://doi.org/10.1080/00949655.2014.922685
  3. 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
  4. 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