Chapter
Apr 26, 2012
Derivation of the Modified Anderson-Darling Test Statistics for the Generalized Extreme Value and Generalized Logistic Distributions
Authors: Hongjoon Shin, Ph.D. [email protected], Taesoon Kim, Ph.D. [email protected], Sooyoung Kim [email protected], Younghun Jung [email protected], and Jun-Haeng Heo, Ph.D. [email protected]Author Affiliations
Publication: World Environmental and Water Resources Congress 2010: Challenges of Change
Abstract
An important problem in water resources engineering is the estimation of the flood magnitude for a certain return period. In flood frequency analysis, an assumed probability distribution is fitted to the available sample data to estimate the flood magnitude at the upper tail corresponding to return periods which are usually much larger than the record length. In most cases, the selection of an appropriate probability distribution is based on goodness-of-fit tests. However, previous goodness-of-fit tests give equal weight to differences between empirical and theoretical distribution functions corresponding to all the observations. In this study, the modified Anderson-Darling test statistics which can give different weights to given data are provided using simulation. And the regression equations for the modified Anderson-Darling test statistics are derived as a function of sample sizes and significance levels for the generalized extreme value and generalized logistic distributions.
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© 2010 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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School of Civil and Environmental Engineering, Yonsei University, Seoul, Korea. E-mail: [email protected]
School of Civil and Environmental Engineering, Yonsei University, Seoul, Korea. E-mail: [email protected]
Ph.D. Candidate, School of Civil and Environmental Engineering, Yonsei University, Seoul, Korea. E-mail: [email protected]
Ph.D. Candidate, School of Civil and Environmental Engineering, Yonsei University, Seoul, Korea. E-mail: [email protected]
Professor, School of Civil and Environmental Engineering, Yonsei University, Seoul, Korea. E-mail: [email protected]
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