Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computation
Scholars usually adopt the method of least squared to model the relationship between a response variable and two or more explanatory variables. Ordinary least squares estimator's performance is good when there is no outliers and multicollinearity in the regression model dataset. Outliers and mu...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Scientific African |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S246822762300025X |
| _version_ | 1811159658300178432 |
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| author | K.C. Arum F.I. Ugwuowo H.E. Oranye T.O. Alakija T.E. Ugah O.C. Asogwa |
| author_facet | K.C. Arum F.I. Ugwuowo H.E. Oranye T.O. Alakija T.E. Ugah O.C. Asogwa |
| author_sort | K.C. Arum |
| collection | DOAJ |
| description | Scholars usually adopt the method of least squared to model the relationship between a response variable and two or more explanatory variables. Ordinary least squares estimator's performance is good when there is no outliers and multicollinearity in the regression model dataset. Outliers and multicollinearity can occur together in a regression model dataset and least squares estimator suffers setback when both problems subsist. This study considers developing a new estimator to address both problems. We combined the principal component estimator (PCE), M-estimator and Kibria-Lukman estimator (KLE) to derive new estimator called robust PC-KL. Robust PC-KL estimator inherits the characteristics of M-estimator, KLE, and PCE which makes it efficient in handling both problems individually and jointly. We examined the performance of the robust PC-KL estimator with other existing estimators using mean squared error (MSE) as performance evaluation criteria through simulation design and real life application. Robust PC-KL estimator outperformed other estimators compared with in this study based on theoretical comparison, simulation design and real life application by having the smallest MSE. |
| first_indexed | 2024-04-10T05:44:53Z |
| format | Article |
| id | doaj.art-fde1cdbb3d914f15a3d877d8de43ff73 |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-04-10T05:44:53Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-fde1cdbb3d914f15a3d877d8de43ff732023-03-06T04:18:21ZengElsevierScientific African2468-22762023-03-0119e01566Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computationK.C. Arum0F.I. Ugwuowo1H.E. Oranye2T.O. Alakija3T.E. Ugah4O.C. Asogwa5Department of Statistics, University of Nigeria, Nsukka, Nigeria; Corresponding author.Department of Statistics, University of Nigeria, Nsukka, NigeriaDepartment of Statistics, University of Nigeria, Nsukka, NigeriaDepartment of Statistics, Yaba College of Technology, Yaba Lagos, NigeriaDepartment of Statistics, University of Nigeria, Nsukka, NigeriaDepartment of Statistics, Federal University, Ndufu Alike, Ikwo, Ebonyi State, NigeriaScholars usually adopt the method of least squared to model the relationship between a response variable and two or more explanatory variables. Ordinary least squares estimator's performance is good when there is no outliers and multicollinearity in the regression model dataset. Outliers and multicollinearity can occur together in a regression model dataset and least squares estimator suffers setback when both problems subsist. This study considers developing a new estimator to address both problems. We combined the principal component estimator (PCE), M-estimator and Kibria-Lukman estimator (KLE) to derive new estimator called robust PC-KL. Robust PC-KL estimator inherits the characteristics of M-estimator, KLE, and PCE which makes it efficient in handling both problems individually and jointly. We examined the performance of the robust PC-KL estimator with other existing estimators using mean squared error (MSE) as performance evaluation criteria through simulation design and real life application. Robust PC-KL estimator outperformed other estimators compared with in this study based on theoretical comparison, simulation design and real life application by having the smallest MSE.http://www.sciencedirect.com/science/article/pii/S246822762300025XRidge estimatorKibria-Lukman estimatorM-estimatorPrincipal componentMulticollinearityOutliers |
| spellingShingle | K.C. Arum F.I. Ugwuowo H.E. Oranye T.O. Alakija T.E. Ugah O.C. Asogwa Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computation Scientific African Ridge estimator Kibria-Lukman estimator M-estimator Principal component Multicollinearity Outliers |
| title | Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computation |
| title_full | Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computation |
| title_fullStr | Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computation |
| title_full_unstemmed | Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computation |
| title_short | Combating outliers and multicollinearity in linear regression model using robust Kibria-Lukman mixed with principal component estimator, simulation and computation |
| title_sort | combating outliers and multicollinearity in linear regression model using robust kibria lukman mixed with principal component estimator simulation and computation |
| topic | Ridge estimator Kibria-Lukman estimator M-estimator Principal component Multicollinearity Outliers |
| url | http://www.sciencedirect.com/science/article/pii/S246822762300025X |
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