A Stochastic Restricted Principal Components Regression Estimator in the Linear Model
We propose a new estimator to combat the multicollinearity in the linear model when there are stochastic linear restrictions on the regression coefficients. The new estimator is constructed by combining the ordinary mixed estimator (OME) and the principal components regression (PCR) estimator, which...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/231506 |
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| _version_ | 1832553556736999424 |
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| author | Daojiang He Yan Wu |
| author_facet | Daojiang He Yan Wu |
| author_sort | Daojiang He |
| collection | DOAJ |
| description | We propose a new estimator to combat the multicollinearity in the linear model when there are stochastic linear restrictions on the regression coefficients. The new estimator is constructed by combining the ordinary mixed estimator (OME) and the principal components regression (PCR) estimator, which is called the stochastic restricted principal components (SRPC) regression estimator. Necessary and sufficient conditions for the superiority of the SRPC estimator over the OME and the PCR estimator are derived in the sense of the mean squared error matrix criterion. Finally, we give a numerical example and a Monte Carlo study to illustrate the performance of the proposed estimator. |
| format | Article |
| id | doaj-art-852d4460193b4fe197a4a038f6152fc4 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-852d4460193b4fe197a4a038f6152fc42025-02-03T05:53:49ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/231506231506A Stochastic Restricted Principal Components Regression Estimator in the Linear ModelDaojiang He0Yan Wu1Department of Statistics, Anhui Normal University, Wuhu 241000, ChinaDepartment of Statistics, Anhui Normal University, Wuhu 241000, ChinaWe propose a new estimator to combat the multicollinearity in the linear model when there are stochastic linear restrictions on the regression coefficients. The new estimator is constructed by combining the ordinary mixed estimator (OME) and the principal components regression (PCR) estimator, which is called the stochastic restricted principal components (SRPC) regression estimator. Necessary and sufficient conditions for the superiority of the SRPC estimator over the OME and the PCR estimator are derived in the sense of the mean squared error matrix criterion. Finally, we give a numerical example and a Monte Carlo study to illustrate the performance of the proposed estimator.http://dx.doi.org/10.1155/2014/231506 |
| spellingShingle | Daojiang He Yan Wu A Stochastic Restricted Principal Components Regression Estimator in the Linear Model The Scientific World Journal |
| title | A Stochastic Restricted Principal Components Regression Estimator in the Linear Model |
| title_full | A Stochastic Restricted Principal Components Regression Estimator in the Linear Model |
| title_fullStr | A Stochastic Restricted Principal Components Regression Estimator in the Linear Model |
| title_full_unstemmed | A Stochastic Restricted Principal Components Regression Estimator in the Linear Model |
| title_short | A Stochastic Restricted Principal Components Regression Estimator in the Linear Model |
| title_sort | stochastic restricted principal components regression estimator in the linear model |
| url | http://dx.doi.org/10.1155/2014/231506 |
| work_keys_str_mv | AT daojianghe astochasticrestrictedprincipalcomponentsregressionestimatorinthelinearmodel AT yanwu astochasticrestrictedprincipalcomponentsregressionestimatorinthelinearmodel AT daojianghe stochasticrestrictedprincipalcomponentsregressionestimatorinthelinearmodel AT yanwu stochasticrestrictedprincipalcomponentsregressionestimatorinthelinearmodel |