A new outlier detection method for spherical data
Adzhar Rambli1, Ibrahim Bin Mohamed2, Abdul Ghapor Hussin3
1Centre of Statistics & Decision Science Studies, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia.
Plos One
|August 24, 2022
Summary
This study introduces a novel k-nearest neighbors method for detecting single and clustered outliers in spherical data, offering a robust alternative to existing discordancy tests.
Area of Science:
- Statistics
- Data Analysis
Background:
- Detecting outlying observations is crucial in data analysis.
- Existing methods for spherical data may have limitations.
Purpose of the Study:
- To propose a new method for detecting outliers in spherical data.
- To generalize the method for identifying patches of outliers.
- To evaluate the performance of the proposed method.
Main Methods:
- The method is based on k-nearest neighbors distance theory.
- Cut-off points for the test statistic were determined.
- Performance was investigated using simulation studies.
Main Results:
- The proposed method effectively detects single outliers in spherical data.
- The method successfully identifies patches of outliers.
- The approach was illustrated using an eye data set.
Conclusions:
- The novel k-nearest neighbors method is a viable alternative for outlier detection in spherical data.
- The method's ability to detect both single and clustered outliers enhances its utility.
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