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Nonparametric tests for detection of high dimensional outliers

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  • Reza Modarres

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.

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  • Reza Modarres, 2022. "Nonparametric tests for detection of high dimensional outliers," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(1), pages 206-227, January.
    • Handle: RePEc:taf:gnstxx:v:34:y:2022:i:1:p:206-227
    DOI: 10.1080/10485252.2022.2026945
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    Cited by:

    1. Reza Modarres, 2024. "Hotelling $$T^2$$ T 2 test in high dimensions with application to Wilks outlier method," Statistical Papers, Springer, vol. 65(8), pages 5203-5218, October.
    2. Modarres, Reza, 2023. "Analysis of distance matrices," Statistics & Probability Letters, Elsevier, vol. 193(C).

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