Detection of outliers in simple circular regression models using the mean circular error statistic
The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we propose a new numerical statistic called mean circular error to identify possible outliers in circular regression models by using a row deletion ap...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2013
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/10160/1/Detection_of_outliers_in_simple_circular_regression_models_using.pdf |
| _version_ | 1825719816617459712 |
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| author | Mohamed, I. Abuzaid, A.H. Hussin, A.G. |
| author_facet | Mohamed, I. Abuzaid, A.H. Hussin, A.G. |
| author_sort | Mohamed, I. |
| collection | UM |
| description | The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we propose a new numerical statistic called mean circular error to identify possible outliers in circular regression models by using a row deletion approach. Through intensive simulation studies, the cut-off points of the statistic are obtained and its power of performance investigated.It is found that the performance improves as the concentration parameter of circular residuals becomes larger or the sample size becomes smaller. As an illustration, the statistic is applied to a wind direction data set. |
| first_indexed | 2024-03-06T05:25:32Z |
| format | Article |
| id | um.eprints-10160 |
| institution | Universiti Malaya |
| language | English |
| last_indexed | 2024-03-06T05:25:32Z |
| publishDate | 2013 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | um.eprints-101602014-11-13T04:12:33Z http://eprints.um.edu.my/10160/ Detection of outliers in simple circular regression models using the mean circular error statistic Mohamed, I. Abuzaid, A.H. Hussin, A.G. QA Mathematics The investigation on the identification of outliers in linear regression models can be extended to those for circular regression case. In this paper, we propose a new numerical statistic called mean circular error to identify possible outliers in circular regression models by using a row deletion approach. Through intensive simulation studies, the cut-off points of the statistic are obtained and its power of performance investigated.It is found that the performance improves as the concentration parameter of circular residuals becomes larger or the sample size becomes smaller. As an illustration, the statistic is applied to a wind direction data set. Taylor & Francis 2013 Article PeerReviewed application/pdf en http://eprints.um.edu.my/10160/1/Detection_of_outliers_in_simple_circular_regression_models_using.pdf Mohamed, I. and Abuzaid, A.H. and Hussin, A.G. (2013) Detection of outliers in simple circular regression models using the mean circular error statistic. Journal of Statistical Computation and Simulation, 83 (2). pp. 269-277. ISSN 0094-9655, |
| spellingShingle | QA Mathematics Mohamed, I. Abuzaid, A.H. Hussin, A.G. Detection of outliers in simple circular regression models using the mean circular error statistic |
| title | Detection of outliers in simple circular regression models using the mean circular error statistic |
| title_full | Detection of outliers in simple circular regression models using the mean circular error statistic |
| title_fullStr | Detection of outliers in simple circular regression models using the mean circular error statistic |
| title_full_unstemmed | Detection of outliers in simple circular regression models using the mean circular error statistic |
| title_short | Detection of outliers in simple circular regression models using the mean circular error statistic |
| title_sort | detection of outliers in simple circular regression models using the mean circular error statistic |
| topic | QA Mathematics |
| url | http://eprints.um.edu.my/10160/1/Detection_of_outliers_in_simple_circular_regression_models_using.pdf |
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