A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem...

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Detalles Bibliográficos
Autores principales: Kibria, B. M. Golam, Lukman, Adewale F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204127/
https://www.ncbi.nlm.nih.gov/pubmed/32399315
http://dx.doi.org/10.1155/2020/9758378
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author Kibria, B. M. Golam
Lukman, Adewale F.
author_facet Kibria, B. M. Golam
Lukman, Adewale F.
author_sort Kibria, B. M. Golam
collection PubMed
description The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model. Theory and simulation results show that, under some conditions, it performs better than both Liu and ridge regression estimators in the smaller MSE sense. Two real-life (chemical and economic) data are analyzed to illustrate the findings of the paper.
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spelling pubmed-72041272020-05-12 A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications Kibria, B. M. Golam Lukman, Adewale F. Scientifica (Cairo) Research Article The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model. Theory and simulation results show that, under some conditions, it performs better than both Liu and ridge regression estimators in the smaller MSE sense. Two real-life (chemical and economic) data are analyzed to illustrate the findings of the paper. Hindawi 2020-04-14 /pmc/articles/PMC7204127/ /pubmed/32399315 http://dx.doi.org/10.1155/2020/9758378 Text en Copyright © 2020 B. M. Golam Kibria and Adewale F. Lukman. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Kibria, B. M. Golam
Lukman, Adewale F.
A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
title A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
title_full A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
title_fullStr A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
title_full_unstemmed A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
title_short A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
title_sort new ridge-type estimator for the linear regression model: simulations and applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204127/
https://www.ncbi.nlm.nih.gov/pubmed/32399315
http://dx.doi.org/10.1155/2020/9758378
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