Identifying multiple outliers in linear functional relationship model using a robust clustering method
Outliers are some observation points outside the usual pattern of the other observations. It is essential to detect outliers as anomalous observations can affect the inference made in the analysis. In this study, we propose an efficient clustering procedure to identify multiple outliers in the linea...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2023
|
| Online Access: | http://journalarticle.ukm.my/22165/ http://journalarticle.ukm.my/22165/1/SL%2020.pdf |
| _version_ | 1848815531358945280 |
|---|---|
| author | Adilah Abdul Ghapor, Yong Zulina Zubairi, Al Mamun, Sayed Md. Siti Fatimah Hassan, Elayaraja Aruchunan, Nurkhairany Amyra Mokhtar, |
| author_facet | Adilah Abdul Ghapor, Yong Zulina Zubairi, Al Mamun, Sayed Md. Siti Fatimah Hassan, Elayaraja Aruchunan, Nurkhairany Amyra Mokhtar, |
| author_sort | Adilah Abdul Ghapor, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Outliers are some observation points outside the usual pattern of the other observations. It is essential to detect outliers as anomalous observations can affect the inference made in the analysis. In this study, we propose an efficient clustering procedure to identify multiple outliers in the linear functional relationship model using the single linkage algorithm with the Euclidean distance as the similarity measure. A new robust cut-off point using the median and median absolute deviation for the tree heights to classify the potential outliers are proposed in this study. Experimental results from the simulation study suggest our proposed method is able to identify the presence of multiple outliers with very small probability of swamping and masking. Application in real data also shows that the proposed clustering method for this linear functional relationship model successfully detects the outliers, thus suggesting the method’s practicality in real-world problems. |
| first_indexed | 2025-11-15T00:51:27Z |
| format | Article |
| id | oai:generic.eprints.org:22165 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:51:27Z |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:221652023-09-06T04:25:05Z http://journalarticle.ukm.my/22165/ Identifying multiple outliers in linear functional relationship model using a robust clustering method Adilah Abdul Ghapor, Yong Zulina Zubairi, Al Mamun, Sayed Md. Siti Fatimah Hassan, Elayaraja Aruchunan, Nurkhairany Amyra Mokhtar, Outliers are some observation points outside the usual pattern of the other observations. It is essential to detect outliers as anomalous observations can affect the inference made in the analysis. In this study, we propose an efficient clustering procedure to identify multiple outliers in the linear functional relationship model using the single linkage algorithm with the Euclidean distance as the similarity measure. A new robust cut-off point using the median and median absolute deviation for the tree heights to classify the potential outliers are proposed in this study. Experimental results from the simulation study suggest our proposed method is able to identify the presence of multiple outliers with very small probability of swamping and masking. Application in real data also shows that the proposed clustering method for this linear functional relationship model successfully detects the outliers, thus suggesting the method’s practicality in real-world problems. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22165/1/SL%2020.pdf Adilah Abdul Ghapor, and Yong Zulina Zubairi, and Al Mamun, Sayed Md. and Siti Fatimah Hassan, and Elayaraja Aruchunan, and Nurkhairany Amyra Mokhtar, (2023) Identifying multiple outliers in linear functional relationship model using a robust clustering method. Sains Malaysiana, 52 (5). pp. 1595-1606. ISSN 0126-6039 http://www.ukm.my/jsm/index.html |
| spellingShingle | Adilah Abdul Ghapor, Yong Zulina Zubairi, Al Mamun, Sayed Md. Siti Fatimah Hassan, Elayaraja Aruchunan, Nurkhairany Amyra Mokhtar, Identifying multiple outliers in linear functional relationship model using a robust clustering method |
| title | Identifying multiple outliers in linear functional relationship model using a robust clustering method |
| title_full | Identifying multiple outliers in linear functional relationship model using a robust clustering method |
| title_fullStr | Identifying multiple outliers in linear functional relationship model using a robust clustering method |
| title_full_unstemmed | Identifying multiple outliers in linear functional relationship model using a robust clustering method |
| title_short | Identifying multiple outliers in linear functional relationship model using a robust clustering method |
| title_sort | identifying multiple outliers in linear functional relationship model using a robust clustering method |
| url | http://journalarticle.ukm.my/22165/ http://journalarticle.ukm.my/22165/ http://journalarticle.ukm.my/22165/1/SL%2020.pdf |