A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator
The multicollinearity problem occurrence of the explanatory variables affects the least-squares (LS) estimator seriously in the regression models. The multicollinearity adverse effects on the LS estimation are also investigated by many authors. Instead of the LS estimator, we propose a new modified...
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The Scientific Association for Studies and Applied Research
2025-04-01
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| Series: | Computational Journal of Mathematical and Statistical Sciences |
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| Online Access: | https://cjmss.journals.ekb.eg/article_414201.html |
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| author | Mohamed Reda Abonazel |
| author_facet | Mohamed Reda Abonazel |
| author_sort | Mohamed Reda Abonazel |
| collection | DOAJ |
| description | The multicollinearity problem occurrence of the explanatory variables affects the least-squares (LS) estimator seriously in the regression models. The multicollinearity adverse effects on the LS estimation are also investigated by many authors. Instead of the LS estimator, we propose a new modified two–parameter Liu (MTPL) estimator to handle the multicollinearity for the regression model based on two shrinkage parameters (k, d). Also, we give the necessary and
sufficient conditions for the outperforming of the proposed MTPL estimator over the LS, ridge, Liu, Kibria-Lukman (KL), modified ridge type (MRT), and modified one–parameter Liu (MOPL) estimators by the scalar mean squared error (MSE) criterion. Optimal biasing parameters of the proposed MTPL estimator are derived. Simulation and real data are used to study the efficiency of the MTPL estimator. The results of the simulation study and two real-life applications show the
superiority of the proposed estimator because the MSE of the proposed estimator is smaller than the other estimators. |
| format | Article |
| id | doaj-art-dbe2bdc815d44b29a542de59b05eff51 |
| institution | DOAJ |
| issn | 2974-3435 2974-3443 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | The Scientific Association for Studies and Applied Research |
| record_format | Article |
| series | Computational Journal of Mathematical and Statistical Sciences |
| spelling | doaj-art-dbe2bdc815d44b29a542de59b05eff512025-08-20T02:42:01ZengThe Scientific Association for Studies and Applied ResearchComputational Journal of Mathematical and Statistical Sciences2974-34352974-34432025-04-014131634710.21608/CJMSS.2025.347818.1096A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu EstimatorMohamed Reda Abonazel0Department of applied statistics and Econometrics, Faculty of Graduate Studies for Statistical Research, Cairo Uniersity, Giza 12613, EgyptThe multicollinearity problem occurrence of the explanatory variables affects the least-squares (LS) estimator seriously in the regression models. The multicollinearity adverse effects on the LS estimation are also investigated by many authors. Instead of the LS estimator, we propose a new modified two–parameter Liu (MTPL) estimator to handle the multicollinearity for the regression model based on two shrinkage parameters (k, d). Also, we give the necessary and sufficient conditions for the outperforming of the proposed MTPL estimator over the LS, ridge, Liu, Kibria-Lukman (KL), modified ridge type (MRT), and modified one–parameter Liu (MOPL) estimators by the scalar mean squared error (MSE) criterion. Optimal biasing parameters of the proposed MTPL estimator are derived. Simulation and real data are used to study the efficiency of the MTPL estimator. The results of the simulation study and two real-life applications show the superiority of the proposed estimator because the MSE of the proposed estimator is smaller than the other estimators.https://cjmss.journals.ekb.eg/article_414201.htmlcompany efficiencykibria-lukman estimatorliu estimatormodified ridge type estimatormonte carlo simulation |
| spellingShingle | Mohamed Reda Abonazel A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator Computational Journal of Mathematical and Statistical Sciences company efficiency kibria-lukman estimator liu estimator modified ridge type estimator monte carlo simulation |
| title | A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator |
| title_full | A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator |
| title_fullStr | A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator |
| title_full_unstemmed | A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator |
| title_short | A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator |
| title_sort | new biased estimation class to combat the multicollinearity in regression models modified two parameter liu estimator |
| topic | company efficiency kibria-lukman estimator liu estimator modified ridge type estimator monte carlo simulation |
| url | https://cjmss.journals.ekb.eg/article_414201.html |
| work_keys_str_mv | AT mohamedredaabonazel anewbiasedestimationclasstocombatthemulticollinearityinregressionmodelsmodifiedtwoparameterliuestimator AT mohamedredaabonazel newbiasedestimationclasstocombatthemulticollinearityinregressionmodelsmodifiedtwoparameterliuestimator |