A Modified New Two-Parameter Estimator in a Linear Regression Model

The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is, when multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified new two-parameter estimator based on...

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Main Authors: Adewale F. Lukman, Kayode Ayinde, Sek Siok Kun, Emmanuel T. Adewuyi
Format: Article
Language:English
Published: Wiley 2019-01-01
Series:Modelling and Simulation in Engineering
Online Access:http://dx.doi.org/10.1155/2019/6342702
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author Adewale F. Lukman
Kayode Ayinde
Sek Siok Kun
Emmanuel T. Adewuyi
author_facet Adewale F. Lukman
Kayode Ayinde
Sek Siok Kun
Emmanuel T. Adewuyi
author_sort Adewale F. Lukman
collection DOAJ
description The literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is, when multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified new two-parameter estimator based on prior information for the vector of parameters is proposed to circumvent the problem of multicollinearity. This new estimator includes the special cases of the ordinary least squares estimator (OLSE), the ridge estimator (RRE), the Liu estimator (LE), the modified ridge estimator (MRE), and the modified Liu estimator (MLE). Furthermore, the superiority of the new estimator over OLSE, RRE, LE, MRE, MLE, and the two-parameter estimator proposed by Ozkale and Kaciranlar (2007) was obtained by using the mean squared error matrix criterion. In conclusion, a numerical example and a simulation study were conducted to illustrate the theoretical results.
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institution Kabale University
issn 1687-5591
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publishDate 2019-01-01
publisher Wiley
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series Modelling and Simulation in Engineering
spelling doaj-art-91a259d057ec41abbcb9efc84790984d2025-08-20T03:37:06ZengWileyModelling and Simulation in Engineering1687-55911687-56052019-01-01201910.1155/2019/63427026342702A Modified New Two-Parameter Estimator in a Linear Regression ModelAdewale F. Lukman0Kayode Ayinde1Sek Siok Kun2Emmanuel T. Adewuyi3Department of Physical Sciences, Landmark University, Omu-Aran, NigeriaDepartment of Statistics, Federal University of Technology, Akure, NigeriaSchool of Mathematical Sciences, Universiti Sains Malaysia, MalaysiaDepartment of Statistics, Federal University of Technology, Akure, NigeriaThe literature has shown that ordinary least squares estimator (OLSE) is not best when the explanatory variables are related, that is, when multicollinearity is present. This estimator becomes unstable and gives a misleading conclusion. In this study, a modified new two-parameter estimator based on prior information for the vector of parameters is proposed to circumvent the problem of multicollinearity. This new estimator includes the special cases of the ordinary least squares estimator (OLSE), the ridge estimator (RRE), the Liu estimator (LE), the modified ridge estimator (MRE), and the modified Liu estimator (MLE). Furthermore, the superiority of the new estimator over OLSE, RRE, LE, MRE, MLE, and the two-parameter estimator proposed by Ozkale and Kaciranlar (2007) was obtained by using the mean squared error matrix criterion. In conclusion, a numerical example and a simulation study were conducted to illustrate the theoretical results.http://dx.doi.org/10.1155/2019/6342702
spellingShingle Adewale F. Lukman
Kayode Ayinde
Sek Siok Kun
Emmanuel T. Adewuyi
A Modified New Two-Parameter Estimator in a Linear Regression Model
Modelling and Simulation in Engineering
title A Modified New Two-Parameter Estimator in a Linear Regression Model
title_full A Modified New Two-Parameter Estimator in a Linear Regression Model
title_fullStr A Modified New Two-Parameter Estimator in a Linear Regression Model
title_full_unstemmed A Modified New Two-Parameter Estimator in a Linear Regression Model
title_short A Modified New Two-Parameter Estimator in a Linear Regression Model
title_sort modified new two parameter estimator in a linear regression model
url http://dx.doi.org/10.1155/2019/6342702
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