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Minimax estimation of a multivariate normal mean under arbitrary quadratic loss

Author

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  • Berger, James

Abstract

Let X be a p-variate (p >= 3) vector normally distributed with mean [theta] and known covariance matrix . It is desired to estimate [theta] under the quadratic loss ([delta] - [theta])t Q([delta] - [theta]), where Q is a known positive definite matrix. A broad class of minimax estimators for [theta] is developed.

Suggested Citation

  • Berger, James, 1976. "Minimax estimation of a multivariate normal mean under arbitrary quadratic loss," Journal of Multivariate Analysis, Elsevier, vol. 6(2), pages 256-264, June.
    • Handle: RePEc:eee:jmvana:v:6:y:1976:i:2:p:256-264
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    Cited by:

    1. Jeff Gill & Gary King, 2004. "What to Do When Your Hessian is Not Invertible," Sociological Methods & Research, , vol. 33(1), pages 54-87, August.
    2. Hansen, Bruce E., 2016. "Efficient shrinkage in parametric models," Journal of Econometrics, Elsevier, vol. 190(1), pages 115-132.
    3. T. Palanisamy & J. Ravichandran, 2015. "A wavelet-based hybrid approach to estimate variance function in heteroscedastic regression models," Statistical Papers, Springer, vol. 56(3), pages 911-932, August.

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