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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...

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Main Authors: Adilah Abdul Ghapor,, Yong Zulina Zubairi,, Al Mamun, Sayed Md., Siti Fatimah Hassan,, Elayaraja Aruchunan,, Nurkhairany Amyra Mokhtar,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2023
Online Access:http://journalarticle.ukm.my/22165/1/SL%2020.pdf
http://journalarticle.ukm.my/22165/
http://www.ukm.my/jsm/index.html
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spelling my-ukm.journal.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
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
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.
format Article
author Adilah Abdul Ghapor,
Yong Zulina Zubairi,
Al Mamun, Sayed Md.
Siti Fatimah Hassan,
Elayaraja Aruchunan,
Nurkhairany Amyra Mokhtar,
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
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,
title 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_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_sort identifying multiple outliers in linear functional relationship model using a robust clustering method
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2023
url http://journalarticle.ukm.my/22165/1/SL%2020.pdf
http://journalarticle.ukm.my/22165/
http://www.ukm.my/jsm/index.html
_version_ 1778162563427598336
score 13.211869

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