Nonparametric tests for detection of high dimensional outliers

Reza Modarres

Journal of Nonparametric Statistics, 2022, vol. 34, issue 1, 206-227

Abstract: Based on the ordered values of the total dissimilarity of each observation from all the others, we present a nonparametric method for 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 visualisation 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 using 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.

Date: 2022
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DOI: 10.1080/10485252.2022.2026945

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