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Comparison of Methods of Selecting the Threshold of Partial Duration Series for GPD Model

GPD 모형 산정을 위한 부분시계열 자료의 임계값 산정방법 비교

  • 엄명진 (연세대학교 대학원 토목공학과) ;
  • 조원철 (연세대학교 공과대학 사회환경시스템공학부) ;
  • 허준행 (연세대학교 공과대학 사회환경시스템공학부)
  • Published : 2008.05.25

Abstract

Generalized Pareto distribution (GPD) is frequently applied in hydrologic extreme value analysis. The main objective of statistics of extremes is the prediction of rare events, and the primary problem has been the estimation of the threshold and the exceedances which were difficult without an accurate method of calculation. In this paper, to obtain the threshold or the exceedances, four methods were considered. For this comparison a GPD model was used to estimate parameters and quantiles for the seven durations (1, 2, 3, 6, 12, 18 and 24 hours) and the ten return periods (2, 3, 5, 10, 20, 30, 50, 70, 80 and 100 years). The parameters and quantiles of the three-parameter generalized Pareto distribution were estimated with three methods (MOM, ML and PWM). To estimate the degree of fit, three methods (K-S, CVM and A-D test) were performed and the relative root mean squared error (RRMSE) was calculated for a Monte Carlo generated sample. Then the performance of these methods were compared with the objective of identifying the best method from their number.

GPD 모형은 수문학 극치확률량 해석에 주로 적용되어 왔다. 극치 통계의 주목적은 드문 사상의 예측이며, 주요 문제점으로는 임계값 또는 임계값 초과치들에 대한 정확한 산정방법이 없어 그 추정이 매우 어렵다는 것이다. 본 연구에서는 임계값 또는 임계값 초과치들을 산정하기 위하여 4가지 방법을 적용하였다. 그 비교를 위하여 GPD 모형에 적용하여 7개의 지속시간(1, 2, 3, 6, 12, 18 및 24시간)과 10개의 재현기간(2, 3, 5, 10, 20, 30, 50, 70, 80 및 100년)에 대한 매개변수 및 Quantile을 추정하였다. 3변수 GPD의 매개변수 및 Quantile을 추정하기 위하여 MOM, ML과 PWM을 적용하였다. 적합도를 추정하기 위하여 K-S, CVM 및 A-D 검정을 수행하였고 Monte Carlo 실험으로 상대 제곱근오차를 산정하였다. 이러한 방법들을 이용하여 임계값 산정방법들을 비교하여 최적화된 방법을 추정하였다.

Keywords

References

  1. Adams, B.J. and Papa, F. (2000). Urban stormwater management planning with analytical probabilistic models, John wiley & Sons, Inc
  2. Choulakian, V. and Stephens, M.A. (2001). "Goodness-of-fit tests for the Generalized Pareto Distribution", Technometrics, Vol. 43, No. 4, pp. 478-484 https://doi.org/10.1198/00401700152672573
  3. Cunnane, C. (1973). "A particular comparison of annual maxima and partial duration series methods of flood frequency prediction", Journal of hydrology, Vol. 18, pp. 257-271 https://doi.org/10.1016/0022-1694(73)90051-6
  4. Danielsson, J., and Haan, L. de, Peng, L. and de Vries, C.G. (2001). "Using a bootstrap method to choose the sample fraction in the tail index estimation", Journal of Mutivariate Analysis, Vol. 76, pp. 226-248 https://doi.org/10.1006/jmva.2000.1903
  5. Drees, H. and Kaufmann, E. (1998). "Selecting the optimal sample fraction in univariate extreme value estimation", Stoch. Proc. and Appl., Vol. 75, pp. 149-172 https://doi.org/10.1016/S0304-4149(98)00017-9
  6. Dutta, K. and Perry, J. (2007). A Tale of Tails An Empirical Analysis of Loss Distribution Models for Estimating Operational Risk Capital, Working Papers, No.06-13, Federal Reserve Bank of Boston
  7. Gomes, M.I. and Oliveira, O. (2001). "The bootstrap methodology in statistics of extremes-Choice of the optimal sample fraction", Extremes, Vol. 4, pp. 331-358 https://doi.org/10.1023/A:1016592028871
  8. Gomes, M.I. and Pestana, D. (2007). "A Sturdy Reduced-Bias Extreme Quantile (VAR) Estimator", Journal of the American Statistical Association, Vol. 102, No. 477, pp. 280-292 https://doi.org/10.1198/016214506000000799
  9. Hall, P. (1982). "On some simple estimates of an exponent of regular variation", Journal of Royal Statistical Society, pp. 37-42
  10. Hall, P. (1990). "Using bootstrap to estimate mean squared error and selecting parameter in nonparametric problems", Journal of Mutivariate Analysis, Vol. 32, pp. 177-203 https://doi.org/10.1016/0047-259X(90)90080-2
  11. Hall, P. and Welsh, A.H. (1985). "Adaptive estimates of parameters of regular variation", Ann. Statist., Vol. 13, pp. 331-341 https://doi.org/10.1214/aos/1176346596
  12. Heaney, J.P., Huber, W.C., Medina, M.A., Jr., Murphy, M.P., Nix, S.J. and Hasan, S.M. (1977). Nationwide Assessment of Combined Sewer Overflows and Urban Stormwater Discharges: Vol. II, Cost Assessment, EPA-600/2-77-064, U.S. Environmental Protection Agency, Cincinnati, OH
  13. Hill, B.M. (1975). "A simple general approach to inference about the tail of a distribution", Ann. Statist., Vol. 3, pp. 1163-1174 https://doi.org/10.1214/aos/1176343247
  14. Hogg, R.V. and Tanis, E.A. (1988). Probability and statistical inference, 3rd edition, Macmillan Publishing Co., New York, NY
  15. Hosking, J.R.M. and Wallis, J.R. (1987). "Parameter and Quantile Estimation for the Generalized Pareto Distribution", Technometrics, Vol. 29, No. 3, pp. 339-349 https://doi.org/10.2307/1269343
  16. Howard, C. and Associates, Ltd. (1979). Analysis and Use of Urban Rainfall in Canada, Report EPS 3-WP-79-4, Water Pollution Control Directorate, Environmental Protection Service, Environment Canada, Ottawa, Ontario
  17. Landwehr, J.M., Matalas, N.C., and Wallis, J.R. (1979). "Estimation of Parameters and Quantiles of Wakeby Distributions", Water Resources Research, Vol. 15, pp. 1361-1379 https://doi.org/10.1029/WR015i006p01361
  18. Madsen, H., Rasmussen, P.F. and Rosbjerg, D. (1997). "Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events-1. At-site modeling", Water resources research, Vol. 33, No. 4, pp. 747-757 https://doi.org/10.1029/96WR03848
  19. Madsen, H., Rosbjerg, D. and Harremoes (1994). "PDS-modeling and regional Bayesian estimation of extreme rainfalls", Nordic Hydrology, Vol. 25, No. 4, pp. 279-300 https://doi.org/10.2166/nh.1994.0009
  20. Moharram, S.H., Gosain, A.K. and Kapoor, P.N. (1993). "A Comparative Study for the Estimators of the Generalized Pareto Distribution" Journal of Hydrology, Vol. 150, pp. 169-185 https://doi.org/10.1016/0022-1694(93)90160-B
  21. Nix, S.J. (1994). Urban Stormwater Modeling and Simulation, Lewis Publishers, Boca Raton, FL
  22. Pickands, J. (1975). "Statistical inference using extreme order statistics", Ann. Statist., Vol. 3, pp. 119-131 https://doi.org/10.1214/aos/1176343003
  23. Rao, A.R. and Hamed, K.H. (2000). Flood frequency analysis, CRC Press, New York
  24. Rasmussen, P.F. and Rosbjerg, D. (1991). "Application of Bayesian principles in regional flood frequency estimation", in Advances in Water Resouces Technology, edited by G. Tsakiris, pp. 66-75
  25. Restrepo-Posada, P.J. and Eagleson, P.S. (1982). "Identification of independent rainstorms", Journal of Hydrology, Vol. 55, pp. 303-319 https://doi.org/10.1016/0022-1694(82)90136-6
  26. Rosbjerg, D., and Madsen H.(1992). “On the choice of threshold level in partial duration series.” Nordic Hydrological Conference, Alta, NHP Rep. 30, pp. 604-615
  27. Rosbjerg, D., Rasmussen, P.F. and Madsen, H. (1991). "Modeling of exceedances in partial duration series", Proceedings of the International Hydrology and Water Resources Symposium, pp. 755-760
  28. Singh, V.P. and Ahmad, M. (2004). "A comparative evaluation of the estimation of the three-parameter generalized pareto distribution", Journal of Statistical Computation and Simulation, Vol. 74, No. 2, pp. 91-106 https://doi.org/10.1080/0094965031000110579
  29. Weissman, I. (1978). "Estimation of Parameters and Large Quantiles Based on the k Largest Observation", Journal of the American Statistical Association, Vol. 73, pp. 812-815 https://doi.org/10.2307/2286285
  30. Willems, P., Guillou, A. and Beirlant, J. (2007). "Bias correction in hydrologic GPD based extreme value analysis by means of a slowly varying function", Journal of Hydrology, Vol. 338, pp. 221-236 https://doi.org/10.1016/j.jhydrol.2007.02.035

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