Outlier detection in circular regression model using minimum spanning tree method
The existence of outliers in circular-circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using minimum spanning tree method. The proposed method is examined via simul...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing Ltd.
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30296/1/2019%20Muhammad_Di__J._Phys.__Conf._Ser._1366_012102.pdf |
| _version_ | 1796994402505195520 |
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| author | Nur Faraidah, Muhammad Di Siti Zanariah, Satari Roslinazairimah, Zakaria |
| author_facet | Nur Faraidah, Muhammad Di Siti Zanariah, Satari Roslinazairimah, Zakaria |
| author_sort | Nur Faraidah, Muhammad Di |
| collection | UMP |
| description | The existence of outliers in circular-circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using minimum spanning tree method. The proposed method is examined via simulation study with different number of sample sizes and level of contaminations. Then, the performance of the proposed method was measured using “success” probability, masking effect, and swamping effect. The results revealed that the proposed method were performed well and able to detect all the outliers planted in various conditions. |
| first_indexed | 2024-03-06T12:47:16Z |
| format | Article |
| id | UMPir30296 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:47:16Z |
| publishDate | 2019 |
| publisher | IOP Publishing Ltd. |
| record_format | dspace |
| spelling | UMPir302962021-07-19T07:43:21Z http://umpir.ump.edu.my/id/eprint/30296/ Outlier detection in circular regression model using minimum spanning tree method Nur Faraidah, Muhammad Di Siti Zanariah, Satari Roslinazairimah, Zakaria QA Mathematics The existence of outliers in circular-circular regression model can lead to many errors, for example in inferences and parameter estimations. Therefore, this study aims to develop new algorithms that can detect outliers by using minimum spanning tree method. The proposed method is examined via simulation study with different number of sample sizes and level of contaminations. Then, the performance of the proposed method was measured using “success” probability, masking effect, and swamping effect. The results revealed that the proposed method were performed well and able to detect all the outliers planted in various conditions. IOP Publishing Ltd. 2019 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30296/1/2019%20Muhammad_Di__J._Phys.__Conf._Ser._1366_012102.pdf Nur Faraidah, Muhammad Di and Siti Zanariah, Satari and Roslinazairimah, Zakaria (2019) Outlier detection in circular regression model using minimum spanning tree method. Journal of Physics: Conference Serie, 1366 (012102). pp. 1-7. ISSN 1742-6596. (Published) http://10.1088/1742-6596/1366/1/012102 |
| spellingShingle | QA Mathematics Nur Faraidah, Muhammad Di Siti Zanariah, Satari Roslinazairimah, Zakaria Outlier detection in circular regression model using minimum spanning tree method |
| title | Outlier detection in circular regression model using minimum spanning tree method |
| title_full | Outlier detection in circular regression model using minimum spanning tree method |
| title_fullStr | Outlier detection in circular regression model using minimum spanning tree method |
| title_full_unstemmed | Outlier detection in circular regression model using minimum spanning tree method |
| title_short | Outlier detection in circular regression model using minimum spanning tree method |
| title_sort | outlier detection in circular regression model using minimum spanning tree method |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/30296/1/2019%20Muhammad_Di__J._Phys.__Conf._Ser._1366_012102.pdf |
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