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Improved minimax estimation of powers of the variance of a multivariate normal distribution under the entropy loss function

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  • Pal, Nabendu
  • Ling, Chiahua

Abstract

Consider the problem of estimating positive powers of the variance of a multivariate normal distribution. We show that Strawderman (Strawderman, 1974) type minimax estimators, which are better than the best affine equivariant estimators under the quadratic loss function, can also be derived under the entropy loss function.

Suggested Citation

  • Pal, Nabendu & Ling, Chiahua, 1995. "Improved minimax estimation of powers of the variance of a multivariate normal distribution under the entropy loss function," Statistics & Probability Letters, Elsevier, vol. 24(3), pages 205-211, August.
    • Handle: RePEc:eee:stapro:v:24:y:1995:i:3:p:205-211
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    References listed on IDEAS

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    1. Rukhin, Andrew L. & Ananda, Malwane M. A., 1992. "Risk behavior of variance estimators in multivariate normal distribution," Statistics & Probability Letters, Elsevier, vol. 13(2), pages 159-166, January.
    2. Guo, Ying (Ingrid) Yueh & Pal, Nabendu, 1992. "A sequence of improvements over the James-Stein estimator," Journal of Multivariate Analysis, Elsevier, vol. 42(2), pages 302-317, August.
    3. Ghosh M. & Sinha B. K., 1987. "Inadmissibility Of The Best Equivariant Estimators Of The Variance-Covariance Matrix, The Precision Matrix, And The Generalized Variance Under Entropy Loss," Statistics & Risk Modeling, De Gruyter, vol. 5(3-4), pages 201-228, April.
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    Cited by:

    1. Pravash Jena & Manas Ranjan Tripathy & Somesh Kumar, 2023. "Point and Interval Estimation of Powers of Scale Parameters for Two Normal Populations with a Common Mean," Statistical Papers, Springer, vol. 64(5), pages 1775-1804, October.

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