A generalized Grubbs‐Beck test statistic for detecting multiple potentially influential low outliers in flood series
A generalized Grubbs‐Beck test statistic for detecting multiple potentially influential low outliers in flood series
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DOI：
10.1002/wrcr.20392
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发表时间：
2013-08
影响因子：
5.4
通讯作者：
T. Cohn;J. England;C. Berenbrock;R. Mason;J. Stedinger;J. Lamontagne
中科院分区：
文献类型：
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作者：
T. Cohn;J. England;C. Berenbrock;R. Mason;J. Stedinger;J. Lamontagne
Grubbs-Beck检验是联邦指南中推荐的，用于在美国洪水流量频率计算中检测低异常值。本文提出了一种对正态数据的Grubbs-Beck检验的推广（类似于Rosner（1983）检验;另见Spencer和McCuen（1996）），可以为识别多个潜在影响的低流量提供一致的标准。在已识别出低离群值的情况下，它们可以表示为“小于”值，并且可以使用删失数据统计技术（例如期望矩算法）来开发频率分布。这种方法可以改善频率分布右尾的拟合，并防止洪水序列中不重要但可能有影响的低流量（PILF）导致的拟合不足，从而使洪水频率分析程序更加稳健。
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.