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.
Full text not available from this repository.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.
| 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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