Nur Faraidah, Muhammad Di and Siti Zanariah, Satari and Roslinazairimah, Zakaria (2019) Outlier detection in circular regression model using minimum spanning tree method. In: ICOAIMS 2019: 2nd International Conference On Applied & Industrial Mathematics And Statistics 2019 , 23 - 25 Julai 2019 , The Zenith Hotel Kuantan. p. 1.. (Unpublished)
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29.1 Outlier detection in circular regression model using minimum spanning tree method.pdf Download (102kB) | Preview |
Abstract
The existence of outliers in a 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 the minimum spanning tree method. The proposed algorithms are extended from Satari’s single-linkage algorithm. The algorithms were examined via simulation studies with different number of sample sizes and level of contaminations. Then, the performances of both algorithms were measured using “success” probability. The results revealed that the proposed methods were performed well and able to detect all the outliers planted in the study.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | Outliers; Circular regression model; Clustering; Single linkage algorithm; Minimum sanning tree |
| Subjects: | Q Science > Q Science (General) |
| Faculty/Division: | Faculty of Industrial Sciences And Technology |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 27 Jun 2019 02:46 |
| Last Modified: | 27 Jun 2019 02:48 |
| URI: | http://umpir.ump.edu.my/id/eprint/24692 |
| Download Statistic: | View Download Statistics |
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