An Outlier Detection Method for Circular Data Using Covratio Statistics
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 parame...
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| Format: | Article |
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Faculty of Science, University of Malaya
2019
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| author | Mokhtar, Nurkhairany Amyra Zubairi, Yong Zulina Hussin, Abdul Ghapor Moslim, Nor Hafizah |
| author_facet | Mokhtar, Nurkhairany Amyra Zubairi, Yong Zulina Hussin, Abdul Ghapor Moslim, Nor Hafizah |
| author_sort | Mokhtar, Nurkhairany Amyra |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T06:01:25Z |
| format | Article |
| id | um.eprints-23971 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:01:25Z |
| publishDate | 2019 |
| publisher | Faculty of Science, University of Malaya |
| record_format | dspace |
| spelling | um.eprints-239712020-03-10T01:29:02Z http://eprints.um.edu.my/23971/ An Outlier Detection Method for Circular Data Using Covratio Statistics Mokhtar, Nurkhairany Amyra Zubairi, Yong Zulina Hussin, Abdul Ghapor Moslim, Nor Hafizah Q Science (General) QA Mathematics 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. Faculty of Science, University of Malaya 2019 Article PeerReviewed 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 https://doi.org/10.22452/mjs.sp2019no2.5 <https://doi.org/10.22452/mjs.sp2019no2.5>. https://doi.org/10.22452/mjs.sp2019no2.5 doi:10.22452/mjs.sp2019no2.5 |
| spellingShingle | Q Science (General) QA Mathematics Mokhtar, Nurkhairany Amyra Zubairi, Yong Zulina Hussin, Abdul Ghapor Moslim, Nor Hafizah An Outlier Detection Method for Circular Data Using Covratio Statistics |
| title | An Outlier Detection Method for Circular Data Using Covratio Statistics |
| title_full | An Outlier Detection Method for Circular Data Using Covratio Statistics |
| title_fullStr | An Outlier Detection Method for Circular Data Using Covratio Statistics |
| title_full_unstemmed | An Outlier Detection Method for Circular Data Using Covratio Statistics |
| title_short | An Outlier Detection Method for Circular Data Using Covratio Statistics |
| title_sort | outlier detection method for circular data using covratio statistics |
| topic | Q Science (General) QA Mathematics |
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