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Activity Number:
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404
- Recent Research in High-Dimensional and Complex Data Analysis
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Type:
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Topic-Contributed
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Date/Time:
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Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #317289
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Title:
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High-Dimensional Rank-Based Inference for Testing Relative Effects
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Author(s):
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Xiaoli Kong* and Solomon Harrar
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Companies:
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Loyola University Chicago and University of Kentucky
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Keywords:
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Nonparametric;
Rank;
Relative effect;
Strong mixing;
MANOVA;
Quadratic forms
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Abstract:
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In this talk, a fully nonparametric rank-based method is introduced for comparing multi-group relative effects. No assumption has been made on the distribution. We only require that the dependencies between the variables satisfy some mild conditions. In particular, to develop the theory we prove a novel result for studying the asymptotic behavior of quadratic forms in ranks. The simulation results show that the developed rank-based method performs comparably well with mean-based methods. It has significantly superior power for heavy-tailed distribution with the possibility of outliers. The results are applied to Electroencephalograph (EEG) data that arose from a study to examine the correlates of genetic predisposition to alcoholism.
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Authors who are presenting talks have a * after their name.
