An Outlier Detection Method for Circular Data Using Covratio Statistics

• Main Authors: Mokhtar, Nurkhairany Amyra, Zubairi, Yong Zulina, Hussin, Abdul Ghapor, Moslim, Nor Hafizah
• Format: Article
• Published: Faculty of Science, University of Malaya 2019
• Subjects: Q Science (General); QA Mathematics
• Online Access: http://eprints.um.edu.my/23971/; https://doi.org/10.22452/mjs.sp2019no2.5
• Institution: Universiti Malaya

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.