TY - JOUR

T1 - Expected probability weighted moment estimator for censored flood data

AU - Jeon, Jong June

AU - Kim, Young Oh

AU - Kim, Yongdai

PY - 2011/8

Y1 - 2011/8

N2 - Two well-known methods for estimating statistical distributions in hydrology are the Method of Moments (MOMs) and the method of probability weighted moments (PWM). This paper is concerned with the case where a part of the sample is censored. One situation where this might occur is when systematic data (e.g. from gauges) are combined with historical data, since the latter are often only reported if they exceed a high threshold. For this problem, three previously derived estimators are the " B17B" estimator, which is a direct modification of MOM to allow for partial censoring; the " partial PWM estimator" , which similarly modifies PWM; and the " expected moments algorithm" estimator, which improves on B17B by replacing a sample adjustment of the censored-data moments with a population adjustment. The present paper proposes a similar modification to the PWM estimator, resulting in the " expected probability weighted moments (EPWM)" estimator. Simulation comparisons of these four estimators and also the maximum likelihood estimator show that the EPWM method is at least competitive with the other four and in many cases the best of the five estimators.

AB - Two well-known methods for estimating statistical distributions in hydrology are the Method of Moments (MOMs) and the method of probability weighted moments (PWM). This paper is concerned with the case where a part of the sample is censored. One situation where this might occur is when systematic data (e.g. from gauges) are combined with historical data, since the latter are often only reported if they exceed a high threshold. For this problem, three previously derived estimators are the " B17B" estimator, which is a direct modification of MOM to allow for partial censoring; the " partial PWM estimator" , which similarly modifies PWM; and the " expected moments algorithm" estimator, which improves on B17B by replacing a sample adjustment of the censored-data moments with a population adjustment. The present paper proposes a similar modification to the PWM estimator, resulting in the " expected probability weighted moments (EPWM)" estimator. Simulation comparisons of these four estimators and also the maximum likelihood estimator show that the EPWM method is at least competitive with the other four and in many cases the best of the five estimators.

KW - Censored data

KW - Flood frequency analysis

KW - GEV distribution

KW - Historical information

KW - Probability weighted moment

UR - http://www.scopus.com/inward/record.url?scp=79960286534&partnerID=8YFLogxK

U2 - 10.1016/j.advwatres.2011.04.003

DO - 10.1016/j.advwatres.2011.04.003

M3 - Article

AN - SCOPUS:79960286534

SN - 0309-1708

VL - 34

SP - 933

EP - 945

JO - Advances in Water Resources

JF - Advances in Water Resources

IS - 8

ER -