An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model
We propose an unbiased restricted estimator that leverages prior information to enhance estimation efficiency for the linear regression model. The statistical properties of the proposed estimator are rigorously examined, highlighting its superiority over several existing methods. A simulation study...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Stats |
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| Online Access: | https://www.mdpi.com/2571-905X/8/1/16 |
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| author | Mustafa I. Alheety HM Nayem B. M. Golam Kibria |
| author_facet | Mustafa I. Alheety HM Nayem B. M. Golam Kibria |
| author_sort | Mustafa I. Alheety |
| collection | DOAJ |
| description | We propose an unbiased restricted estimator that leverages prior information to enhance estimation efficiency for the linear regression model. The statistical properties of the proposed estimator are rigorously examined, highlighting its superiority over several existing methods. A simulation study is conducted to evaluate the performance of the estimators, and real-world data on total national research and development expenditures by country are analyzed to illustrate the findings. Both the simulation results and real-data analysis demonstrate that the proposed estimator consistently outperforms the alternatives considered in this study. |
| format | Article |
| id | doaj-art-55a69d29fb3f4b8a9618afec1d129a2e |
| institution | OA Journals |
| issn | 2571-905X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Stats |
| spelling | doaj-art-55a69d29fb3f4b8a9618afec1d129a2e2025-08-20T01:48:54ZengMDPI AGStats2571-905X2025-02-01811610.3390/stats8010016An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression ModelMustafa I. Alheety0HM Nayem1B. M. Golam Kibria2Department of Mathematics, College of Education for Pure Sciences, University of Anbar, Anbar 31001, IraqDepartment of Mathematics and Statistics, Florida International University, Miami, FL 33199, USADepartment of Mathematics and Statistics, Florida International University, Miami, FL 33199, USAWe propose an unbiased restricted estimator that leverages prior information to enhance estimation efficiency for the linear regression model. The statistical properties of the proposed estimator are rigorously examined, highlighting its superiority over several existing methods. A simulation study is conducted to evaluate the performance of the estimators, and real-world data on total national research and development expenditures by country are analyzed to illustrate the findings. Both the simulation results and real-data analysis demonstrate that the proposed estimator consistently outperforms the alternatives considered in this study.https://www.mdpi.com/2571-905X/8/1/16linear modelMSEmulticollinearityrestricted least-squares estimatorunbiased ridge estimator |
| spellingShingle | Mustafa I. Alheety HM Nayem B. M. Golam Kibria An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model Stats linear model MSE multicollinearity restricted least-squares estimator unbiased ridge estimator |
| title | An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model |
| title_full | An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model |
| title_fullStr | An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model |
| title_full_unstemmed | An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model |
| title_short | An Unbiased Convex Estimator Depending on Prior Information for the Classical Linear Regression Model |
| title_sort | unbiased convex estimator depending on prior information for the classical linear regression model |
| topic | linear model MSE multicollinearity restricted least-squares estimator unbiased ridge estimator |
| url | https://www.mdpi.com/2571-905X/8/1/16 |
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