A Simulation Study of Some Restricted Estimators in Restricted Linear Regression Model
When the multicollinearity exists in linear regression model, the result of the Restricted Least Square estimator (RLS) is unstable. So that, more researchers proposed the restricted biased estimators to improve the efficiency of RLS estimator. In this paper, Some of biased restricted estimators ha...
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| Format: | Article |
| Language: | English |
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Tikrit University
2021-07-01
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| Series: | Tikrit Journal of Pure Science |
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| Online Access: | https://tjpsj.org/index.php/tjps/article/view/147 |
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| author | Bader Aboud Mohammad Mustafa Ismaeel Naif |
| author_facet | Bader Aboud Mohammad Mustafa Ismaeel Naif |
| author_sort | Bader Aboud Mohammad |
| collection | DOAJ |
| description |
When the multicollinearity exists in linear regression model, the result of the Restricted Least Square estimator (RLS) is unstable. So that, more researchers proposed the restricted biased estimators to improve the efficiency of RLS estimator. In this paper, Some of biased restricted estimators have been introduced to study the performance of them. The simulation study has been given to compare these estimators. According to simulation study, we found that, the shrinkage restricted ridge regression (SRRE) estimator which proposed by Baber and Mustafa [1], has good properties comparing with other restricted estimators that given in this study. A Numerical example has been considered to illustrate the performance of these estimators
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| format | Article |
| id | doaj-art-4da0f2f6bf4a458da9ba5f83ad0129c6 |
| institution | DOAJ |
| issn | 1813-1662 2415-1726 |
| language | English |
| publishDate | 2021-07-01 |
| publisher | Tikrit University |
| record_format | Article |
| series | Tikrit Journal of Pure Science |
| spelling | doaj-art-4da0f2f6bf4a458da9ba5f83ad0129c62025-08-20T03:16:51ZengTikrit UniversityTikrit Journal of Pure Science1813-16622415-17262021-07-0126310.25130/tjps.v26i3.147A Simulation Study of Some Restricted Estimators in Restricted Linear Regression ModelBader Aboud MohammadMustafa Ismaeel Naif When the multicollinearity exists in linear regression model, the result of the Restricted Least Square estimator (RLS) is unstable. So that, more researchers proposed the restricted biased estimators to improve the efficiency of RLS estimator. In this paper, Some of biased restricted estimators have been introduced to study the performance of them. The simulation study has been given to compare these estimators. According to simulation study, we found that, the shrinkage restricted ridge regression (SRRE) estimator which proposed by Baber and Mustafa [1], has good properties comparing with other restricted estimators that given in this study. A Numerical example has been considered to illustrate the performance of these estimators https://tjpsj.org/index.php/tjps/article/view/147MulticollinearityLinear regression modelRestricted biased estimatorsMean square errorSimulation study |
| spellingShingle | Bader Aboud Mohammad Mustafa Ismaeel Naif A Simulation Study of Some Restricted Estimators in Restricted Linear Regression Model Tikrit Journal of Pure Science Multicollinearity Linear regression model Restricted biased estimators Mean square error Simulation study |
| title | A Simulation Study of Some Restricted Estimators in Restricted Linear Regression Model |
| title_full | A Simulation Study of Some Restricted Estimators in Restricted Linear Regression Model |
| title_fullStr | A Simulation Study of Some Restricted Estimators in Restricted Linear Regression Model |
| title_full_unstemmed | A Simulation Study of Some Restricted Estimators in Restricted Linear Regression Model |
| title_short | A Simulation Study of Some Restricted Estimators in Restricted Linear Regression Model |
| title_sort | simulation study of some restricted estimators in restricted linear regression model |
| topic | Multicollinearity Linear regression model Restricted biased estimators Mean square error Simulation study |
| url | https://tjpsj.org/index.php/tjps/article/view/147 |
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