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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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Scientifica |
| Online Access: | http://dx.doi.org/10.1155/2020/9758378 |
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| author | B. M. Golam Kibria Adewale F. Lukman |
| author_facet | B. M. Golam Kibria Adewale F. Lukman |
| author_sort | B. M. Golam Kibria |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-78a26bb3180a4283b68a048323734e66 |
| institution | Kabale University |
| issn | 2090-908X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Scientifica |
| spelling | doaj-art-78a26bb3180a4283b68a048323734e662025-02-03T01:04:06ZengWileyScientifica2090-908X2020-01-01202010.1155/2020/97583789758378A New Ridge-Type Estimator for the Linear Regression Model: Simulations and ApplicationsB. M. Golam Kibria0Adewale F. Lukman1Department of Mathematics and Statistics, Florida International University, Miami, FL, USADepartment of Physical Sciences, Landmark University, Omu-Aran, NigeriaThe 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.http://dx.doi.org/10.1155/2020/9758378 |
| spellingShingle | B. M. Golam Kibria Adewale F. Lukman A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications Scientifica |
| 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 |
| url | http://dx.doi.org/10.1155/2020/9758378 |
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