An efficient method of identification of influential observations in multiple linear regression and its application to real data
Influential observations (IOs) are those observations which either alone or together with several other observations have detrimental effect on the computed values of various estimates. As such, it is very important to detect their presence. Several methods have been proposed to identify IOs that in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/23368/ http://journalarticle.ukm.my/23368/1/SD%2019.pdf |
| _version_ | 1848815828898676736 |
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| author | Habshah Mid, Hasan Talib Hendi, Uraibi, Hassan Jayanthi Arasan, Ismaeel, Shelan Saied |
| author_facet | Habshah Mid, Hasan Talib Hendi, Uraibi, Hassan Jayanthi Arasan, Ismaeel, Shelan Saied |
| author_sort | Habshah Mid, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Influential observations (IOs) are those observations which either alone or together with several other observations have detrimental effect on the computed values of various estimates. As such, it is very important to detect their presence. Several methods have been proposed to identify IOs that include the fast improvised influential distance (FIID). The FIID method has been shown to be more efficient than some existing methods. Nonetheless, the shortcoming of the FIID method is that, it is computationally not stable, still suffers from masking and swamping effects, time consuming issues and not using proper cut-off point. As a solution to this problem, a new robust version of influential distance method (RFIID) which is based on Reweighted Fast Consistent and High Breakdown (RFCH) estimator is proposed. The results of real data and Monte Carlo simulation study indicate that the RFIID able to correctly separate the IOs from the rest of data with the least computational running times, least swamping effect and no masking effect compared to the other methods in this study. |
| first_indexed | 2025-11-15T00:56:11Z |
| format | Article |
| id | oai:generic.eprints.org:23368 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:56:11Z |
| publishDate | 2023 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:233682024-04-17T08:42:33Z http://journalarticle.ukm.my/23368/ An efficient method of identification of influential observations in multiple linear regression and its application to real data Habshah Mid, Hasan Talib Hendi, Uraibi, Hassan Jayanthi Arasan, Ismaeel, Shelan Saied Influential observations (IOs) are those observations which either alone or together with several other observations have detrimental effect on the computed values of various estimates. As such, it is very important to detect their presence. Several methods have been proposed to identify IOs that include the fast improvised influential distance (FIID). The FIID method has been shown to be more efficient than some existing methods. Nonetheless, the shortcoming of the FIID method is that, it is computationally not stable, still suffers from masking and swamping effects, time consuming issues and not using proper cut-off point. As a solution to this problem, a new robust version of influential distance method (RFIID) which is based on Reweighted Fast Consistent and High Breakdown (RFCH) estimator is proposed. The results of real data and Monte Carlo simulation study indicate that the RFIID able to correctly separate the IOs from the rest of data with the least computational running times, least swamping effect and no masking effect compared to the other methods in this study. Penerbit Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/23368/1/SD%2019.pdf Habshah Mid, and Hasan Talib Hendi, and Uraibi, Hassan and Jayanthi Arasan, and Ismaeel, Shelan Saied (2023) An efficient method of identification of influential observations in multiple linear regression and its application to real data. Sains Malaysiana, 52 (12). pp. 3589-3602. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol52num12_2023/contentsVol52num12_2023.html |
| spellingShingle | Habshah Mid, Hasan Talib Hendi, Uraibi, Hassan Jayanthi Arasan, Ismaeel, Shelan Saied An efficient method of identification of influential observations in multiple linear regression and its application to real data |
| title | An efficient method of identification of influential observations in multiple linear regression and its application to real data |
| title_full | An efficient method of identification of influential observations in multiple linear regression and its application to real data |
| title_fullStr | An efficient method of identification of influential observations in multiple linear regression and its application to real data |
| title_full_unstemmed | An efficient method of identification of influential observations in multiple linear regression and its application to real data |
| title_short | An efficient method of identification of influential observations in multiple linear regression and its application to real data |
| title_sort | efficient method of identification of influential observations in multiple linear regression and its application to real data |
| url | http://journalarticle.ukm.my/23368/ http://journalarticle.ukm.my/23368/ http://journalarticle.ukm.my/23368/1/SD%2019.pdf |