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A generalized Grubbs‐Beck test statistic for detecting multiple potentially influential low outliers in flood series
Author(s) -
Cohn T. A.,
England J. F.,
Berenbrock C. E.,
Mason R. R.,
Stedinger J. R.,
Lamontagne J. R.
Publication year - 2013
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/wrcr.20392
Subject(s) - outlier , test statistic , statistic , flood myth , statistics , generalization , series (stratigraphy) , computer science , econometrics , statistical hypothesis testing , mathematics , data mining , geography , geology , mathematical analysis , paleontology , archaeology
The Grubbs‐Beck test is recommended by the federal guidelines for detection of low outliers in flood flow frequency computation in the United States. This paper presents a generalization of the Grubbs‐Beck test for normal data (similar to the Rosner (1983) test; see also Spencer and McCuen (1996)) that can provide a consistent standard for identifying multiple potentially influential low flows. In cases where low outliers have been identified, they can be represented as “less‐than” values, and a frequency distribution can be developed using censored‐data statistical techniques, such as the Expected Moments Algorithm. This approach can improve the fit of the right‐hand tail of a frequency distribution and provide protection from lack‐of‐fit due to unimportant but potentially influential low flows (PILFs) in a flood series, thus making the flood frequency analysis procedure more robust.