Modified One-Parameter Liu Estimator for the Linear Regression Model
Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a sing...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/9574304 |
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| _version_ | 1832551249711464448 |
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| author | Adewale F. Lukman B. M. Golam Kibria Kayode Ayinde Segun L. Jegede |
| author_facet | Adewale F. Lukman B. M. Golam Kibria Kayode Ayinde Segun L. Jegede |
| author_sort | Adewale F. Lukman |
| collection | DOAJ |
| description | Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter. Theoretical comparisons, real-life application, and simulation results show that it consistently dominates the usual Liu estimator. Under some conditions, it performs better than the ridge regression estimators in the smaller MSE sense. Two real-life data are analyzed to illustrate the findings of the paper and the performances of the estimators assessed by MSE and the mean squared prediction error. The application result agrees with the theoretical and simulation results. |
| format | Article |
| id | doaj-art-4d406ab2a717416c80593d2cc7dd5b51 |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-4d406ab2a717416c80593d2cc7dd5b512025-02-03T06:04:37ZengWileyModelling and Simulation in Engineering1687-55911687-56052020-01-01202010.1155/2020/95743049574304Modified One-Parameter Liu Estimator for the Linear Regression ModelAdewale F. Lukman0B. M. Golam Kibria1Kayode Ayinde2Segun L. Jegede3Department of Physical Sciences, Landmark University, Omu-Aran, NigeriaDepartment of Mathematics and Statistics, Florida International University, USADepartment of Statistics, Federal University of Technology, Akure, NigeriaDepartment of Physical Sciences, Landmark University, Omu-Aran, NigeriaMotivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter. Theoretical comparisons, real-life application, and simulation results show that it consistently dominates the usual Liu estimator. Under some conditions, it performs better than the ridge regression estimators in the smaller MSE sense. Two real-life data are analyzed to illustrate the findings of the paper and the performances of the estimators assessed by MSE and the mean squared prediction error. The application result agrees with the theoretical and simulation results.http://dx.doi.org/10.1155/2020/9574304 |
| spellingShingle | Adewale F. Lukman B. M. Golam Kibria Kayode Ayinde Segun L. Jegede Modified One-Parameter Liu Estimator for the Linear Regression Model Modelling and Simulation in Engineering |
| title | Modified One-Parameter Liu Estimator for the Linear Regression Model |
| title_full | Modified One-Parameter Liu Estimator for the Linear Regression Model |
| title_fullStr | Modified One-Parameter Liu Estimator for the Linear Regression Model |
| title_full_unstemmed | Modified One-Parameter Liu Estimator for the Linear Regression Model |
| title_short | Modified One-Parameter Liu Estimator for the Linear Regression Model |
| title_sort | modified one parameter liu estimator for the linear regression model |
| url | http://dx.doi.org/10.1155/2020/9574304 |
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