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The superiority of empirical Bayes estimator of parameters in linear model

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  • Zhang, Weiping
  • Wei, Laisheng
  • Yang, Yaning
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    Abstract

    In this paper, an empirical Bayes (EB) estimator is derived for the estimable functions of the parameters in normal linear model. The superiority of the EB estimator over ordinary least-squares (LS) estimator is investigated under mean square error matrix (MSEM) criterion.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 72 (2005)
    Issue (Month): 1 (April)
    Pages: 43-50

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    Handle: RePEc:eee:stapro:v:72:y:2005:i:1:p:43-50

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    Related research

    Keywords: Linear model Empirical Bayes estimator Least-squares estimator Mean-square error matrix;

    References

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    1. Wei, Laisheng & Chen, Jiahua, 2003. "Empirical Bayes estimation and its superiority for two-way classification model," Statistics & Probability Letters, Elsevier, vol. 63(2), pages 165-175, June.
    2. Ghosh M. & Saleh A.K.Md.E. & Sen P.K., 1989. "Empirical Bayes Subset Estimation In Regression Models," Statistics & Risk Modeling, De Gruyter, vol. 7(1-2), pages 15-36, February.
    3. Laisheng Wei & Shunpu Zhang, 1995. "The convergence rates of empirical Bayes estimation in a multiple linear regression model," Annals of the Institute of Statistical Mathematics, Springer, vol. 47(1), pages 81-97, January.
    4. L. Wei & G. Trenkler, 1995. "Mean square error matrix superiority of Empirical Bayes Estimators under misspecification," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 4(1), pages 187-205, June.
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