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- W2776125812 abstract "We recommend methods of discrimination between some three-parameter distributions used in hydro-meteorological frequency modeling. Discriminations are between model pairs belonging to the group (generalized extreme value (GEV), Pearson Type III (P3), generalized logistic (GLO)). To assess the fit of these distributions to data, the Akaike information criterion, Bayesian information criterion, and (or) goodness-of-fit measures are commonly employed. However, it is difficult to estimate the discrimination power and bias of these methods when used with three-parameter distributions. Consequently, we propose two alternative tools and assess their performance. Both tools are based on a sample transformation to normality followed by applying a powerful statistic for testing normality, such as the Shapiro-Wilk or the probability plot correlation coefficient statistic. While arriving at recommendations for discriminating between the (GEV, GLO) and (P3, GLO) pairs of models, we show that the discrimination power between the P3 and GEV distributions can be rather low." @default.
- W2776125812 created "2018-01-05" @default.
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- W2776125812 date "2018-05-01" @default.
- W2776125812 modified "2023-10-14" @default.
- W2776125812 title "Discrimination between a group of three-parameter distributions for hydro-meteorological frequency modeling" @default.
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- W2776125812 doi "https://doi.org/10.1139/cjce-2017-0416" @default.
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