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Activity Number: 499 - Nonparametric Multiple Comparison in High Dimensions with Model Uncertainity
Type: Topic Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #322669
Title: Nonparametric Multiple Comparisons with Effect Size Considerations
Author(s): Kimihiro Noguchi* and Riley Abel
Companies: Western Washington University and Western Washington University
Keywords: Effect Size ; Multiple comparisons ; Nonparametric inference

Nonparametric multiple comparisons allow researchers to compare multiple samples with virtually no distributional assumption on the data using the idea of relative effects. However, from the practical point of view, the raw estimated relative effect difference or its standardized version may be difficult to interpret, possibly making nonparametric multiple comparisons less attractive to practitioners. Thus, we suggest a modified approach that can accommodate various effect size measurements for comparing relative effects. In particular, we present modified test statistics and their finite-sample approximations. Moreover, we examine an application of the modified approach to a recent neuropsychological study.

Authors who are presenting talks have a * after their name.

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