Modified Liu estimators in the linear regression model: An application to Tobacco data.

<h4>Background</h4>The problem of multicollinearity in multiple linear regression models arises when the predictor variables are correlated among each other. The variance of the ordinary least squared estimator become unstable in such situation. In order to mitigate the problem of multic...

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Autores principales:	Iqra Babar, Hamdi Ayed, Sohail Chand, Muhammad Suhail, Yousaf Ali Khan, Riadh Marzouki
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f6d766c06a9442249dc18c5304e746ec
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spelling	oai:doaj.org-article:f6d766c06a9442249dc18c5304e746ec2021-12-02T20:16:19ZModified Liu estimators in the linear regression model: An application to Tobacco data.1932-620310.1371/journal.pone.0259991https://doaj.org/article/f6d766c06a9442249dc18c5304e746ec2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259991https://doaj.org/toc/1932-6203<h4>Background</h4>The problem of multicollinearity in multiple linear regression models arises when the predictor variables are correlated among each other. The variance of the ordinary least squared estimator become unstable in such situation. In order to mitigate the problem of multicollinearity, Liu regression is widely used as a biased method of estimation with shrinkage parameter 'd'. The optimal value of shrinkage parameter plays a vital role in bias-variance trade-off.<h4>Limitation</h4>Several estimators are available in literature for the estimation of shrinkage parameter. But the existing estimators do not perform well in terms of smaller mean squared error when the problem of multicollinearity is high or severe.<h4>Methodology</h4>In this paper, some new estimators for the shrinkage parameter are proposed. The proposed estimators are the class of estimators that are based on quantile of the regression coefficients. The performance of the new estimators is compared with the existing estimators through Monte Carlo simulation. Mean squared error and mean absolute error is considered as evaluation criteria of the estimators. Tobacco dataset is used as an application to illustrate the benefits of the new estimators and support the simulation results.<h4>Findings</h4>The new estimators outperform the existing estimators in most of the considered scenarios including high and severe cases of multicollinearity. 95% mean prediction interval of all the estimators is also computed for the Tobacco data. The new estimators give the best mean prediction interval among all other estimators.<h4>The implications of the findings</h4>We recommend the use of new estimators to practitioners when the problem of high to severe multicollinearity exists among the predictor variables.Iqra BabarHamdi AyedSohail ChandMuhammad SuhailYousaf Ali KhanRiadh MarzoukiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259991 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Iqra Babar Hamdi Ayed Sohail Chand Muhammad Suhail Yousaf Ali Khan Riadh Marzouki Modified Liu estimators in the linear regression model: An application to Tobacco data.
description	<h4>Background</h4>The problem of multicollinearity in multiple linear regression models arises when the predictor variables are correlated among each other. The variance of the ordinary least squared estimator become unstable in such situation. In order to mitigate the problem of multicollinearity, Liu regression is widely used as a biased method of estimation with shrinkage parameter 'd'. The optimal value of shrinkage parameter plays a vital role in bias-variance trade-off.<h4>Limitation</h4>Several estimators are available in literature for the estimation of shrinkage parameter. But the existing estimators do not perform well in terms of smaller mean squared error when the problem of multicollinearity is high or severe.<h4>Methodology</h4>In this paper, some new estimators for the shrinkage parameter are proposed. The proposed estimators are the class of estimators that are based on quantile of the regression coefficients. The performance of the new estimators is compared with the existing estimators through Monte Carlo simulation. Mean squared error and mean absolute error is considered as evaluation criteria of the estimators. Tobacco dataset is used as an application to illustrate the benefits of the new estimators and support the simulation results.<h4>Findings</h4>The new estimators outperform the existing estimators in most of the considered scenarios including high and severe cases of multicollinearity. 95% mean prediction interval of all the estimators is also computed for the Tobacco data. The new estimators give the best mean prediction interval among all other estimators.<h4>The implications of the findings</h4>We recommend the use of new estimators to practitioners when the problem of high to severe multicollinearity exists among the predictor variables.
format	article
author	Iqra Babar Hamdi Ayed Sohail Chand Muhammad Suhail Yousaf Ali Khan Riadh Marzouki
author_facet	Iqra Babar Hamdi Ayed Sohail Chand Muhammad Suhail Yousaf Ali Khan Riadh Marzouki
author_sort	Iqra Babar
title	Modified Liu estimators in the linear regression model: An application to Tobacco data.
title_short	Modified Liu estimators in the linear regression model: An application to Tobacco data.
title_full	Modified Liu estimators in the linear regression model: An application to Tobacco data.
title_fullStr	Modified Liu estimators in the linear regression model: An application to Tobacco data.
title_full_unstemmed	Modified Liu estimators in the linear regression model: An application to Tobacco data.
title_sort	modified liu estimators in the linear regression model: an application to tobacco data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f6d766c06a9442249dc18c5304e746ec
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Modified Liu estimators in the linear regression model: An application to Tobacco data.

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