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 -