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Bandwidth selection for discontinuity point estimation in density

확률밀도함수의 불연속점 추정을 위한 띠폭 선택

  • Huh, Jib (Department of Information & Statistics, Duksung Women's University)
  • 허집 (덕성여자대학교 정보통계학과)
  • Received : 2011.11.19
  • Accepted : 2012.01.02
  • Published : 2012.01.31

Abstract

In the case that the probability density function has a discontinuity point, Huh (2002) estimated the location and jump size of the discontinuity point based on the difference between the right and left kernel density estimators using the one-sided kernel function. In this paper, we consider the cross-validation, made by the right and left maximum likelihood cross-validations, for the bandwidth selection in order to estimate the location and jump size of the discontinuity point. This method is motivated by the one-sided cross-validation of Hart and Yi (1998). The finite sample performance is illustrated by simulated example.

Huh (2002)는 확률밀도함수가 하나의 불연속점을 가질 때, 한쪽방향커널함수를 이용하여 확률 밀도함수의 오른쪽과 왼쪽 커널추정량을 제시하여 그 차를 최대로 하는 점을 불연속점의 위치추정량으로 제안하였다. 커널추정량의 평활모수인 띠폭의 선택의 중요함은 익히 알려져 있다. 최대가능도 교차타당성은 확률밀도함수의 커널추정량에서 띠폭 선택의 기준으로 널리 쓰여지고 있다. 본 연구에서는 한쪽방향커널함수를 이용한 확률밀도함수의 오른쪽과 왼쪽 커널추정량들의 띠폭의 선택 방법을 Hart와 Yi (1998)의 한쪽방향교차타당성의 방법론을 최대가능도교차타당성에 적용하여 제안하고자 한다. 소표본 모의실험을 통하여 연구결과를 제시하고자 한다.

Keywords

References

  1. Cline, D. B. H. and Hart, J. D. (1991). Kernel estimation of densities with discontinuities or discontinuous derivatives. Statistics, 22, 69-84. https://doi.org/10.1080/02331889108802286
  2. Gijbels, I. and Goderniaux, A. C. (2004a). Bandwidth selection for change point estimation in nonparametric regression. Technometrics, 46, 76-86. https://doi.org/10.1198/004017004000000130
  3. Gijbels, I. and Goderniaux, A. C. (2004a). Bootstrap test for change points in nonparametric regression. Journal of Nonparametric Statistics, 16, 591-611. https://doi.org/10.1080/10485250310001626088
  4. Gijbels, I. and Goderniaux, A. C. (2005). Data-driven discontinuity detection in derivatives of a regression function. Communications in Statistics-Theory and Methods, 33, 851-871. https://doi.org/10.1081/STA-120028730
  5. Hart, J. D. and Yi, S. (1998). One-sided cross-validation. Journal of the American Statistical Association, 93, 620-631. https://doi.org/10.1080/01621459.1998.10473715
  6. Huh, J. (2002). Nonparametric discontinuity point estimation in density or density derivatives. Journal of the Korean Statistical Society, 31, 261-276.
  7. Huh, J. (2007). Nonparametric detection algorithm of discontinuity points in the variance function. Journal of the Korean Data & Information Science Society, 18, 669-678.
  8. Huh, J. (2010a). Estimation of the number of discontinuity points based on likelihood. Journal of the Korean Data & Information Science Society, 21, 51-59.
  9. Huh, J. (2010b). Detection of a change point based on local-likelihood. Journal of Multivariate Analysis, 101, 1681-1700. https://doi.org/10.1016/j.jmva.2010.02.007
  10. Huh, J. (2011). Likelihood based estimation of the log-variance function with a change point. Submitted to Journal of Statistical Planning and Inference.
  11. Huh, J. and Carri`ere, K. C. (2002). Estimation of regression functions with a discontinuity in a derivative with local polynomial fits. Statistics and Probability Letters, 56, 329-343. https://doi.org/10.1016/S0167-7152(02)00017-2
  12. Huh, J. and Park, B. U. (2004). Detection of change point with local polynomial fits for random design case. Australian and New Zealand Journal of Statistics, 46, 425-441. https://doi.org/10.1111/j.1467-842X.2004.00340.x
  13. Jose, C. T. and Ismail, B. (1999). Change points in nonparametric regression functions. Communication in Statistics-Theory and Methods, 28, 1883-1902. https://doi.org/10.1080/03610929908832393
  14. Kim, J. T., Choi, H. and Huh, J. (2003). Detection of change-points by local linear regression fit. The Korean Communications in Statistics, 10, 31-38. https://doi.org/10.5351/CKSS.2003.10.1.031
  15. Lee, C. S., Chang, C. and Park, Y. W. (2010). Estimates for parameter changes in a uniform model with a generalized uniform outlier. Journal of the Korean Data & Information Science Society, 21, 581-687.
  16. Loader, C. R. (1996). Change point estimation using nonparametric regression. Annals of Statistics, 24, 1667-1678. https://doi.org/10.1214/aos/1032298290
  17. Muller, H G. (1992). Change-points in nonparametric regression analysis. Annals of Statistics, 20, 737-761. https://doi.org/10.1214/aos/1176348654
  18. Muller, H. G. and Wang, J. L. (1990). Nonparametric analysis of changes in hazard rates for censored survival data: An alternative to change-point models. Biometrika, 77, 305-314. https://doi.org/10.1093/biomet/77.2.305
  19. Otsu, T and Xu, K.-L. (2010). Estimation and inference of discontinuity in density. preprint.
  20. Schuster, E. F. (1985). Incorporating support constraints into nonparametric estimators of densities. Communications in Statistics-Theory and Methods, 14, 1123-1136. https://doi.org/10.1080/03610928508828965

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