An Outlier Detection Method for Circular Data Using Covratio Statistics

Mokhtar, Nurkhairany Amyra and Zubairi, Yong Zulina and Hussin, Abdul Ghapor and Moslim, Nor Hafizah (2019) An Outlier Detection Method for Circular Data Using Covratio Statistics. Malaysian Journal of Science, 38. pp. 46-54. ISSN 1394-3065, DOI

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The existence of outlier may affect data aberrantly. However, outlier detection problem has been frequently discussed for linear data but limited on circular data. Thus, this paper discusses an outlier detection method on circular data. We focus on circular data with equal error concentration parameters where the data is studied using linear functional relationship model. In this paper, the data and the error terms are distributed with von Mises distribution. We modify the covratio statistics in which the correction factor is applied to the estimation of concentration parameter. We develop the cut-off equation based on the 5% upper percentile of the covratio statistics and the power of performance of outlier detection is examined by a Monte Carlo simulation study. The simulation result shows that the power of performance increases when the concentration and the level of contamination increase. The applicability of the proposed method is illustrated by using the wind direction data collected from the Holderness Coastline at the Humberside Coast in North Sea, United Kingdom. © 2019 Malaysian Abstracting and Indexing System. All rights reserved.

Item Type: Article
Funders: University of Malaya (Grant GPF006H-2018), Ministry of Education Malaysia, GE STEM grant (vot no. 07397)
Uncontrolled Keywords: Circular data; Covratio statistics; Linear functional relationship model; Outlier detection
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: Centre for Foundation Studies in Science > Mathematics Division
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 10 Mar 2020 01:29
Last Modified: 10 Mar 2020 01:29

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