Review on outliers identification methods for univariate circular biological data
Circular data are common in biological studies which are involved angle and direction measurements. An outlier in circular biological data mostly related to the abnormality of the data set. The existence of outliers may affect the final outcome of a data analysis. Thus, an outliers’ identification m...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
ASTES Publishers
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30293/1/2020%20Satari%20and%20Ku%20Khalif%20ASTESJ_050212.pdf http://umpir.ump.edu.my/id/eprint/30293/ https://dx.doi.org/10.25046/aj050212 https://dx.doi.org/10.25046/aj050212 |
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| Summary: | Circular data are common in biological studies which are involved angle and direction measurements. An outlier in circular biological data mostly related to the abnormality of the data set. The existence of outliers may affect the final outcome of a data analysis. Thus, an outliers’ identification method is essential in circular biological data to determine the stage of abnormality for the sample that has been studied. Past studies were mostly focusing on detecting outliers for multivariate circular biological data. However, identifying outlier for univariate data is more essential in the abnormality stage investigation. In this study, outliers’ identification methods for univariate circular biological data have been reviewed. The strength and weaknesses of the methods are investigated and discussed. |
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