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A Family Of Improved Ordinary Ridge Estimators

Author

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  • Ullah, A.
  • Vinod, H. D.
  • Kadiyala, R. K.

Abstract

This paper studies the Mean Squared Error (MSE) properties of .a proposed family of Ordinary Ridge Estimators (OREs) of the coefficients in the linear regression. We make extensive use of G( ) functions to provide both exact and asymptotic approximations to the MSE. Using these results we propose a new set of OREs whose MSE is smaller than that of the Ordinary least squares (OLS) estimator. These improved estimators can be used when faced with the multicollinearity problem. A simulation study is also done to further analyse the MSE of the proposed estimators compared with some of the existing OREs.

Suggested Citation

  • Ullah, A. & Vinod, H. D. & Kadiyala, R. K., 1978. "A Family Of Improved Ordinary Ridge Estimators," Econometric Institute Archives 272169, Erasmus University Rotterdam.
    • Handle: RePEc:ags:eureia:272169
    DOI: 10.22004/ag.econ.272169
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    References listed on IDEAS

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    1. Judge, George G & Bock, M E, 1976. "A Comparison of Traditional and Stein-Rule Estimators under Weighted Squared Error Loss," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 17(1), pages 234-240, February.
    2. Vinod, Hrishikesh D, 1978. "A Survey of Ridge Regression and Related Techniques for Improvements over Ordinary Least Squares," The Review of Economics and Statistics, MIT Press, vol. 60(1), pages 121-131, February.
    3. Ullah, Aman & Ullah, Shobha, 1978. "Double k-Class Estimators of Coefficients in Linear Regression," Econometrica, Econometric Society, vol. 46(3), pages 705-722, May.
    4. Kadane, Joseph B, 1971. "Comparison of k-Class Estimators when the Disturbances are Small," Econometrica, Econometric Society, vol. 39(5), pages 723-737, September.
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    Cited by:

    1. Nusrat Yasmin & B. M. Golam Kibria, 2025. "Performance of Some Improved Estimators and their Robust Versions in Presence of Multicollinearity and Outliers," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(1), pages 173-219, May.

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