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New Stochastic Restricted Biased Regression Estimators

Author

Listed:
  • Issam Dawoud

    (Department of Mathematics, Al-Aqsa University, Gaza 4051, Palestine)

  • Hussein Eledum

    (Department of Statistics, Faculty of Science, University of Tabuk, Tabuk 47512, Saudi Arabia)

Abstract

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.

Suggested Citation

  • Issam Dawoud & Hussein Eledum, 2024. "New Stochastic Restricted Biased Regression Estimators," Mathematics, MDPI, vol. 13(1), pages 1-18, December.
    • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:15-:d:1551838
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