Fast improvised influential distance for the identification of influential observations in multiple linear regression
Influential observations (IO) are those observations that are responsible for misleading conclusions about the fitting of a multiple linear regression model. The existing IO identification methods such as influential distance (ID) is not very successful in detecting IO. It is suspected that the ID e...
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Penerbit Universiti Kebangsaan Malaysia
2021
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Online Access: | http://psasir.upm.edu.my/id/eprint/97310/1/ABSTRACT.pdf http://psasir.upm.edu.my/id/eprint/97310/ https://www.ukm.my/jsm/english_journals/vol50num7_2021/vol50num7_2021pg2085-2094.html |
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my.upm.eprints.973102022-09-12T08:40:24Z http://psasir.upm.edu.my/id/eprint/97310/ Fast improvised influential distance for the identification of influential observations in multiple linear regression Midi, Habshah Sani, Muhammad Ismaeel, Shelan Saied Arasan, Jayanthi Influential observations (IO) are those observations that are responsible for misleading conclusions about the fitting of a multiple linear regression model. The existing IO identification methods such as influential distance (ID) is not very successful in detecting IO. It is suspected that the ID employed inefficient method with long computational running time for the identification of the suspected IO at the initial step. Moreover, this method declares good leverage observations as IO, resulting in misleading conclusion. In this paper, we proposed fast improvised influential distance (FIID) that can successfully identify IO, good leverage observations, and regular observations with shorter computational running time. Monte Carlo simulation study and real data examples show that the FIID correctly identify genuine IO in multiple linear regression model with no masking and a negligible swamping rate. Penerbit Universiti Kebangsaan Malaysia 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/97310/1/ABSTRACT.pdf Midi, Habshah and Sani, Muhammad and Ismaeel, Shelan Saied and Arasan, Jayanthi (2021) Fast improvised influential distance for the identification of influential observations in multiple linear regression. Sains Malaysiana, 50 (7). 2085 - 2094. ISSN 0126-6039 https://www.ukm.my/jsm/english_journals/vol50num7_2021/vol50num7_2021pg2085-2094.html 10.17576/jsm-2021-5007-22 |
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Influential observations (IO) are those observations that are responsible for misleading conclusions about the fitting of a multiple linear regression model. The existing IO identification methods such as influential distance (ID) is not very successful in detecting IO. It is suspected that the ID employed inefficient method with long computational running time for the identification of the suspected IO at the initial step. Moreover, this method declares good leverage observations as IO, resulting in misleading conclusion. In this paper, we proposed fast improvised influential distance (FIID) that can successfully identify IO, good leverage observations, and regular observations with shorter computational
running time. Monte Carlo simulation study and real data examples show that the FIID correctly identify genuine IO in multiple linear regression model with no masking and a negligible swamping rate. |
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Article |
author |
Midi, Habshah Sani, Muhammad Ismaeel, Shelan Saied Arasan, Jayanthi |
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Midi, Habshah Sani, Muhammad Ismaeel, Shelan Saied Arasan, Jayanthi Fast improvised influential distance for the identification of influential observations in multiple linear regression |
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Midi, Habshah Sani, Muhammad Ismaeel, Shelan Saied Arasan, Jayanthi |
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Midi, Habshah |
title |
Fast improvised influential distance for the identification of influential observations in multiple linear regression |
title_short |
Fast improvised influential distance for the identification of influential observations in multiple linear regression |
title_full |
Fast improvised influential distance for the identification of influential observations in multiple linear regression |
title_fullStr |
Fast improvised influential distance for the identification of influential observations in multiple linear regression |
title_full_unstemmed |
Fast improvised influential distance for the identification of influential observations in multiple linear regression |
title_sort |
fast improvised influential distance for the identification of influential observations in multiple linear regression |
publisher |
Penerbit Universiti Kebangsaan Malaysia |
publishDate |
2021 |
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http://psasir.upm.edu.my/id/eprint/97310/1/ABSTRACT.pdf http://psasir.upm.edu.my/id/eprint/97310/ https://www.ukm.my/jsm/english_journals/vol50num7_2021/vol50num7_2021pg2085-2094.html |
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