Review on Outliers Identification Methods for Univariate Circular Biological Data

Abstract

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.

Multiple Desirable Methods in Outlier Detection of Univariate Data With R Source Codes

“…In the future, it should be clearly stated whether outliers have been considered and the details of the detection method (Leys et al, 2013 ). Furthermore, since each method has its own advantages and disadvantages (Yang et al, 2019 ; Ahmed et al, 2020 ; Satari and Khalif, 2020 ), we should have a clear understanding of the characteristics of the data before choosing which detection method to use. It is necessary to become familiar with a wide range of detection methods, including those not covered in this study, and use them according to the data.…”