Minimax Estimation of the Mean Matrix of the Matrix Variate Normal Distribution under the Divergence Loss Function

Shokofeh Zinodiny (), Sadegh Rezaei () and Saralees Nadarajah ()
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Shokofeh Zinodiny: Amirkabir University of Technology - Iran
Sadegh Rezaei: Amirkabir University of Technology - Iran
Saralees Nadarajah: University of Manchester - UK

Statistica, 2017, vol. 77, issue 4, 369-384

Abstract: The problem of estimating the mean matrix of a matrix-variate normal distribution with a covariance matrix is considered under two loss functions. We construct a class of empirical Bayes estimators which are better than the maximum likelihood estimator under the first loss function and hence show that the maximum likelihood estimator is inadmissible. We find a general class of minimax estimators. Also we give a class of estimators that improve on the maximum likelihood estimator under the second loss function and hence show that the maximum likelihood estimator is inadmissible.

Keywords: Empirical Bayes estimation; Matrix variate normal distribution; Mean matrix; Minimax estimation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:77:y:2017:i:4:p:369-384

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