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Activity Number:
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203
- Advances in Nonparametric Testing
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #322348
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Title:
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Nonparametric Tests for Detection of High-Dimensional Outliers
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Author(s):
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Reza Modarres*
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Companies:
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George Washington University
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Keywords:
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Wilks Outlier Test;
Dissimilarity Measure;
Interpoint Distance
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Abstract:
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Based on the ordered values of the total dissimilarity of each observation from all the others, we present a nonparametric method for the detection of high dimensional outliers. We provide algorithms to obtain the distribution of the test statistic based on the percentile bootstrap and offer an outlier visualization plot as a nonparametric graphical tool for detecting outliers in a data set. We compare the interpoint distance outlier test (IDOT) with five competing methods under four distributions and a real data set. IDOT shows the best performance for outlier detection in terms of the average number of the outliers detected and the probability of the correct identification.
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Authors who are presenting talks have a * after their name.
