New Stochastic Restricted Biased Regression Estimators
In this paper, we propose three stochastic restricted biased estimators for the linear regression model. These new estimators generalize the least squares estimator, mixed estimator, and biased estimator. We derive the necessary and sufficient conditions for the superiority of the proposed estimator...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/1/15 |
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| author | Issam Dawoud Hussein Eledum |
| author_facet | Issam Dawoud Hussein Eledum |
| author_sort | Issam Dawoud |
| collection | DOAJ |
| description | In this paper, we propose three stochastic restricted biased estimators for the linear regression model. These new estimators generalize the least squares estimator, mixed estimator, and biased estimator. We derive the necessary and sufficient conditions for the superiority of the proposed estimators over existing ones, as well as their relative superiority among each other, using the mean squared error matrix as a criterion. A simulation study is conducted to validate the theoretical findings, and two real-world examples are provided to demonstrate the practical advantages of the proposed estimators. |
| format | Article |
| id | doaj-art-e52006788b0e4c4b90c937edc06fc2f8 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-e52006788b0e4c4b90c937edc06fc2f82025-01-10T13:17:58ZengMDPI AGMathematics2227-73902024-12-011311510.3390/math13010015New Stochastic Restricted Biased Regression EstimatorsIssam Dawoud0Hussein Eledum1Department of Mathematics, Al-Aqsa University, Gaza 4051, PalestineDepartment of Statistics, Faculty of Science, University of Tabuk, Tabuk 47512, Saudi ArabiaIn this paper, we propose three stochastic restricted biased estimators for the linear regression model. These new estimators generalize the least squares estimator, mixed estimator, and biased estimator. We derive the necessary and sufficient conditions for the superiority of the proposed estimators over existing ones, as well as their relative superiority among each other, using the mean squared error matrix as a criterion. A simulation study is conducted to validate the theoretical findings, and two real-world examples are provided to demonstrate the practical advantages of the proposed estimators.https://www.mdpi.com/2227-7390/13/1/15biased estimatormixed estimatorstochastic restricted biased estimatormean squared error matrix |
| spellingShingle | Issam Dawoud Hussein Eledum New Stochastic Restricted Biased Regression Estimators Mathematics biased estimator mixed estimator stochastic restricted biased estimator mean squared error matrix |
| title | New Stochastic Restricted Biased Regression Estimators |
| title_full | New Stochastic Restricted Biased Regression Estimators |
| title_fullStr | New Stochastic Restricted Biased Regression Estimators |
| title_full_unstemmed | New Stochastic Restricted Biased Regression Estimators |
| title_short | New Stochastic Restricted Biased Regression Estimators |
| title_sort | new stochastic restricted biased regression estimators |
| topic | biased estimator mixed estimator stochastic restricted biased estimator mean squared error matrix |
| url | https://www.mdpi.com/2227-7390/13/1/15 |
| work_keys_str_mv | AT issamdawoud newstochasticrestrictedbiasedregressionestimators AT husseineledum newstochasticrestrictedbiasedregressionestimators |