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)
|
Pdf
2019 Muhammad_Di__J._Phys.__Conf._Ser._1366_012102.pdf Download (1MB) | Preview |
Abstract
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.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics |
| Faculty/Division: | Center for Mathematical Science |
| Depositing User: | Ms. Siti Zanariah Satari |
| Date Deposited: | 19 Jul 2021 07:43 |
| Last Modified: | 19 Jul 2021 07:43 |
| URI: | http://umpir.ump.edu.my/id/eprint/30296 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |