Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Nonparametric homogeneity tests of two distributions for credit rating model validation

신용평가모형에서 두 분포함수의 동일성 검정을 위한 비모수적인 검정방법

  • Hong, Chong-Sun (Department of Statistics, Sungkyunkwan University) ;
  • Kim, Ji-Hoon (Research Institute of Applied Statistics, Sungkyunkwan University)
  • 홍종선 (성균관대학교 통계학과) ;
  • 김지훈 (성균관대학교 응용통계연구소)
  • Published : 2009.03.31
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Abstract

Kolmogorov-Smirnov (K-S) statistic has been widely used for testing homogeneity of two distributions in the credit rating models. Joseph (2005) used K-S statistic to obtain validation criteria which is most well-known. There are other homogeneity test statistics such as the Cramer-von Mises, Anderson-Darling, and Watson statistics. In this paper, these statistics are introduced and applied to obtain criterion of these statistics by extending Joseph (2005)'s work. Another set of alternative criterion is suggested according to various sample sizes, type a error rates, and the ratios of bads and goods by using the simulated data under the similar situation as real credit rating data. We compare and explore among Joseph's criteria and two sets of the proposed criterion and discuss their applications.

신용평가모형에서 두 집단의 판별력 검정방법 중의 하나로 두 분포함수의 동일성 검정을 위한 비모수적인 Kolmogorov-Smirnov (K-S) 검정방법이 대표적으로 적용되고 있다. 본 연구에서는 신용평가모형에서 두 분포함수의 동일성 검정을 위하여 K-S 검정 방법 외에 Cramer-Von Mises, Anderson-Darling, Watson 검정방법들을 소개하고 Joseph (2005)의 기준에 대응하는 판단기준을 제안한다. 또한 신용평가 자료와 유사한 상황 하에서의 모의실험을 통해서 불량률, 표본크기 그리고 제II종 오류율을 고려한 대안적인 판단기준을 제시하고 그 적용방법에 대해서 살펴본다.

Keywords

References

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