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Other Classes of Minimax Estimators of Variance Covariance Matrix in Multivariate Normal Distribution

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  • Hara, Hisayuki

Abstract

It is well known that the best equivariant estimator of the variance covariance matrix of the multivariate normal distribution with respect to the full affine group of transformation is not even minimax. Some minimax estimators have been proposed. Here we treat this problem in the framework of a multivariate analysis of variance (MANOVA) model and give other classes of minimax estimators.

Suggested Citation

  • Hara, Hisayuki, 2001. "Other Classes of Minimax Estimators of Variance Covariance Matrix in Multivariate Normal Distribution," Journal of Multivariate Analysis, Elsevier, vol. 77(2), pages 175-186, May.
    • Handle: RePEc:eee:jmvana:v:77:y:2001:i:2:p:175-186
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    References listed on IDEAS

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    1. Sheena, Yo & Takemura, Akimichi, 1992. "Inadmissibility of non-order-preserving orthogonally invariant estimators of the covariance matrix in the case of Stein's loss," Journal of Multivariate Analysis, Elsevier, vol. 41(1), pages 117-131, April.
    2. 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.
    3. Perron, F., 1992. "Minimax estimators of a covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 43(1), pages 16-28, October.
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