TY - GEN

T1 - Comparison of the probability plot correlation coefficient test statistics for the general extreme value distribution

AU - Kim, Sooyoung

AU - Heo, Jun Haeng

N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.

PY - 2010

Y1 - 2010

N2 - A proper probability distribution for estimating a quantile is selected by the goodness of fit tests in frequency analysis. The probability plot correlation coefficient(PPCC) test has been known as powerful and easy test among the goodness of fit tests. Generally, the PPCC test statistics are affected by significance levels, sample sizes, plotting position formulas, and shape parameters in case that a given distribution includes a shape parameter. Therefore, it is important to select an exact plotting position formula for the PPCC test statistics for a given probability distribution. After Cunnane(1978) defined the plotting position that related with the mean of data, many researches have accomplished about the plotting position formulas considered the influence of coefficients of skewness related with shape parameters. In this study, the PPCC test statistics are derived by using a plotting position formula developed from theoretical reduced variates with a term of a coefficient of skewness for the general extreme value(GEV) distribution. In addition, the PPCC test statistics are estimated by considering various sample sizes, significance levels, and shape parameters of the GEV distribution. The performance of derived PPCC test statistics is evaluated by estimating the rejection rate of population from Monte Carlo simulation.

AB - A proper probability distribution for estimating a quantile is selected by the goodness of fit tests in frequency analysis. The probability plot correlation coefficient(PPCC) test has been known as powerful and easy test among the goodness of fit tests. Generally, the PPCC test statistics are affected by significance levels, sample sizes, plotting position formulas, and shape parameters in case that a given distribution includes a shape parameter. Therefore, it is important to select an exact plotting position formula for the PPCC test statistics for a given probability distribution. After Cunnane(1978) defined the plotting position that related with the mean of data, many researches have accomplished about the plotting position formulas considered the influence of coefficients of skewness related with shape parameters. In this study, the PPCC test statistics are derived by using a plotting position formula developed from theoretical reduced variates with a term of a coefficient of skewness for the general extreme value(GEV) distribution. In addition, the PPCC test statistics are estimated by considering various sample sizes, significance levels, and shape parameters of the GEV distribution. The performance of derived PPCC test statistics is evaluated by estimating the rejection rate of population from Monte Carlo simulation.

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U2 - 10.1061/41114(371)253

DO - 10.1061/41114(371)253

M3 - Conference contribution

AN - SCOPUS:77955002890

SN - 9780784411148

T3 - World Environmental and Water Resources Congress 2010: Challenges of Change - Proceedings of the World Environmental and Water Resources Congress 2010

SP - 2456

EP - 2466

BT - World Environmental and Water Resources Congress 2010

T2 - World Environmental and Water Resources Congress 2010: Challenges of Change

Y2 - 16 May 2010 through 20 May 2010

ER -