On the estimation of ridge penalty in linear regression: Simulation and application
According to existing literature, the ordinary least squares (OLS) estimators are not the best in presence of multicollinearity. The inability of OLS estimators against multicollinearity has paved the way for the development of various ridge type estimators for circumventing the problem of multicoll...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-10-01
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| Series: | Kuwait Journal of Science |
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| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410824000981 |
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| author | Khan M.S. Ali A. Suhail M. |
| author_facet | Khan M.S. Ali A. Suhail M. |
| author_sort | Khan M.S. |
| collection | DOAJ |
| description | According to existing literature, the ordinary least squares (OLS) estimators are not the best in presence of multicollinearity. The inability of OLS estimators against multicollinearity has paved the way for the development of various ridge type estimators for circumventing the problem of multicollinearity. In this paper improved two-parameter ridge (ITPR) estimators are proposed. A simulation study is used to evaluate the performance of proposed estimators based on minimum mean squared error (MSE) criterion. The simulative results reveal that, based on minimum MSE, ITPR2 was the most efficient estimator compared to the considered estimators in the study. Finally, a real-life dataset is analyzed to demonstrate the applications of the proposed estimators and also checked their efficacy for mitigation of multicollinearity. © 2024 |
| format | Article |
| id | doaj-art-af9c9f06cd034257ad00e833e6ea11f3 |
| institution | Kabale University |
| issn | 2307-4108 2307-4116 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-af9c9f06cd034257ad00e833e6ea11f32025-08-20T03:30:48ZengElsevierKuwait Journal of Science2307-41082307-41162024-10-0151410027310.1016/j.kjs.2024.100273On the estimation of ridge penalty in linear regression: Simulation and applicationKhan M.S.Ali A.Suhail M.According to existing literature, the ordinary least squares (OLS) estimators are not the best in presence of multicollinearity. The inability of OLS estimators against multicollinearity has paved the way for the development of various ridge type estimators for circumventing the problem of multicollinearity. In this paper improved two-parameter ridge (ITPR) estimators are proposed. A simulation study is used to evaluate the performance of proposed estimators based on minimum mean squared error (MSE) criterion. The simulative results reveal that, based on minimum MSE, ITPR2 was the most efficient estimator compared to the considered estimators in the study. Finally, a real-life dataset is analyzed to demonstrate the applications of the proposed estimators and also checked their efficacy for mitigation of multicollinearity. © 2024https://www.sciencedirect.com/science/article/pii/S2307410824000981linear regression modelmean square errormonte carlo simulationmulticollinearitypredictionridge regressiontwo parameter ridge estimators |
| spellingShingle | Khan M.S. Ali A. Suhail M. On the estimation of ridge penalty in linear regression: Simulation and application Kuwait Journal of Science linear regression model mean square error monte carlo simulation multicollinearity prediction ridge regression two parameter ridge estimators |
| title | On the estimation of ridge penalty in linear regression: Simulation and application |
| title_full | On the estimation of ridge penalty in linear regression: Simulation and application |
| title_fullStr | On the estimation of ridge penalty in linear regression: Simulation and application |
| title_full_unstemmed | On the estimation of ridge penalty in linear regression: Simulation and application |
| title_short | On the estimation of ridge penalty in linear regression: Simulation and application |
| title_sort | on the estimation of ridge penalty in linear regression simulation and application |
| topic | linear regression model mean square error monte carlo simulation multicollinearity prediction ridge regression two parameter ridge estimators |
| url | https://www.sciencedirect.com/science/article/pii/S2307410824000981 |
| work_keys_str_mv | AT khanms ontheestimationofridgepenaltyinlinearregressionsimulationandapplication AT alia ontheestimationofridgepenaltyinlinearregressionsimulationandapplication AT suhailm ontheestimationofridgepenaltyinlinearregressionsimulationandapplication |