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An approach to improving the James-Stein estimator

Author

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  • Kubokawa, Tatsuya

Abstract

For the mean vector of a p-variate normal distribution (p [greater, double equals] 3), the generalized Bayes estimators dominating the James-Stein estimator under quadratic loss are given based on the methods of Brown, Brewster and Zidek for estimating a normal variance.

Suggested Citation

  • Kubokawa, Tatsuya, 1991. "An approach to improving the James-Stein estimator," Journal of Multivariate Analysis, Elsevier, vol. 36(1), pages 121-126, January.
    • Handle: RePEc:eee:jmvana:v:36:y:1991:i:1:p:121-126
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    Citations

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    Cited by:

    1. Chaturvedi Anoop & Mishra Sandeep, 2019. "Generalized Bayes Estimation Of Spatial Autoregressive Models," Statistics in Transition New Series, Statistics Poland, vol. 20(2), pages 15-32, June.
    2. Maruyama Yuzo, 2003. "A robust generalized Bayes estimator improving on the James-Stein estimator for spherically symmetric distributions," Statistics & Risk Modeling, De Gruyter, vol. 21(1), pages 69-78, January.
    3. Maruyama, Yuzo, 2004. "Stein's idea and minimax admissible estimation of a multivariate normal mean," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 320-334, February.
    4. Ohtani, Kazuhiro, 1996. "Further improving the Stein-rule estimator using the Stein variance estimator in a misspecified linear regression model," Statistics & Probability Letters, Elsevier, vol. 29(3), pages 191-199, September.

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