A new outlier detection method for spherical data

Rambli, Adzhar and Mohamed, Ibrahim and Hussin, Abdul Ghapor (2022) A new outlier detection method for spherical data. PLoS ONE, 17 (8). ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0273144.

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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.

Item Type: Article
Funders: state that Universiti Teknologi MARA Research Grant [600-IRMI/FRGS 5/3 (353/2019)], UM IIRG Research Grant [IIRG002A-19FNW]
Uncontrolled Keywords: Spherical data; Data set; Statistic
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 12 Sep 2023 03:03
Last Modified: 12 Sep 2023 03:03
URI: http://eprints.um.edu.my/id/eprint/41170

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