NDLI: Power of the Mann–Whitney test for detecting a shift in median or mean of hydro-meteorological data

Content Provider	Springer Nature Link
Author	Yue, S. Wang, C. Y.
Copyright Year	2002
Abstract	The non-parametric Mann–Whitney (MW) statistic test has been popularly used to assess the significance of a shift in median or mean of hydro-meteorological time series. It has been considered that the test is more suitable for non-normally distributed data and it may be not sensitive to the distribution type of sample data. However, no evidence has been provided to demonstrate these. This study investigates the power of the test in various circumstances by means of Monte Carlo simulation. Simulation results demonstrate that the power of the test is very sensitive to various properties of sample data. The power depends on the pre-assigned significance level, magnitude of a shift, sample size, and its occurrence position within a time series; and it is also strongly affected by the variation, skewness, and distribution type of a time series. The bigger the magnitude of a shift, the more powerful the test is; the larger the sample size, the more powerful the test is; and the bigger the variation within a time series, the less power the test has. The test has the highest power if a shift occurs at the midpoint of a time series. For the samples with different distribution types, the power of the test is dramatically different. The test has the highest power for time series with the extreme value type III (EV3) distribution while it indicates the lowest power for time series with the lognormal distribution.
Starting Page	307
Ending Page	323
Page Count	17
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	16
Issue Number	4
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2002-08-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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