Simultaneous multiple comparisons with a control using median differences and permutation tests

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Scott J. Richter, Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: Permutation methods using median differences for simultaneous pairwise comparisons with a control are investigated. Simulation results suggest that the permutation methods are generally more powerful than the Dunnett procedure when data are from nonnormal distributions. A new procedure is shown to provide strong control of the familywise error rate, and have highest power for detecting the treatment that differs most from the control, for certain nonnormal distributions. Step-down permutation procedures, which have greater power to detect treatment differences with the control, are also proposed and examined. The procedures are illustrated using an example from the applied literature.

Additional Information

Statistics and Probability Letters, 83(4) 1167-1173
Language: English
Date: 2013
Nonparametric simultaneous inference, Multiple comparisons with a control, Median difference, Permutation test

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