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| Titel |
Exploiting the information content of hydrological ''outliers'' for goodness-of-fit testing |
| VerfasserIn |
F. Laio, P. Allamano, P. Claps |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 14, no. 10 ; Nr. 14, no. 10 (2010-10-12), S.1909-1917 |
| Datensatznummer |
250012443
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| Publikation (Nr.) |
 copernicus.org/hess-14-1909-2010.pdf |
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| Zusammenfassung |
| Validation of probabilistic models based on goodness-of-fit tests is an
essential step for the frequency analysis of extreme events. The outcome of
standard testing techniques, however, is mainly determined by the behavior of
the hypothetical model, FX(x), in the central part of the distribution,
while the behavior in the tails of the distribution, which is indeed very
relevant in hydrological applications, is relatively unimportant for the
results of the tests. The maximum-value test, originally proposed as a
technique for outlier detection, is a suitable, but seldom applied, technique
that addresses this problem. The test is specifically targeted to verify if
the maximum (or minimum) values in the sample are consistent with the
hypothesis that the distribution FX(x) is the real parent distribution.
The application of this test is hindered by the fact that the critical values
for the test should be numerically obtained when the parameters of FX(x)
are estimated on the same sample used for verification, which is the standard
situation in hydrological applications. We propose here a simple,
analytically explicit, technique to suitably account for this effect, based
on the application of censored L-moments estimators of the parameters. We
demonstrate, with an application that uses artificially generated samples,
the superiority of this modified maximum-value test with respect to the
standard version of the test. We also show that the test has comparable or
larger power with respect to other goodness-of-fit tests (e.g., chi-squared
test, Anderson-Darling test, Fung and Paul test), in particular when dealing
with small samples (sample size lower than 20–25) and when the parent
distribution is similar to the distribution being tested. |
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