A new outlier detection method for spherical data
Document Type
Article
Publication Date
8-1-2022
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.
Keywords
Spherical data, Data set, Statistic
Divisions
Science
Funders
state that Universiti Teknologi MARA Research Grant [600-IRMI/FRGS 5/3 (353/2019)],UM IIRG Research Grant [IIRG002A-19FNW]
Publication Title
PLoS ONE
Volume
17
Issue
8
Publisher
Public Library of Science
Publisher Location
1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA