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A new outlier detection method for spherical data

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  • Adzhar Rambli
  • Ibrahim Bin Mohamed
  • Abdul Ghapor Hussin

Abstract

In this study, we propose a new method to detect outlying observations in spherical data. The method is based on the k-nearest neighbours distance theory. The proposed method is a good alternative to the existing tests of discordancy for detecting outliers in spherical data. In addition, the new method can be generalized to identify a patch of outliers in the data. We obtain the cut-off points and investigate the performance of the test statistic via simulation. The proposed test performs well in detecting a single and a patch of outliers in spherical data. As an illustration, we apply the method on an eye data set.

Suggested Citation

  • Adzhar Rambli & Ibrahim Bin Mohamed & Abdul Ghapor Hussin, 2022. "A new outlier detection method for spherical data," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-12, August.
    • Handle: RePEc:plo:pone00:0273144
    DOI: 10.1371/journal.pone.0273144
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    References listed on IDEAS

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    1. D. Collett, 1980. "Outliers in Circular Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 50-57, March.
    2. Adzhar Rambli & Ali H M Abuzaid & Ibrahim Bin Mohamed & Abdul Ghapor Hussin, 2016. "Procedure for Detecting Outliers in a Circular Regression Model," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-10, April.
    3. N. I. Fisher & T. Lewis & M. E. Willcox, 1981. "Tests of Discordancy for Samples from Fisher's Distribution on the Sphere," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 30(3), pages 230-237, November.
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