This paper derives the risk functions of a class of shrinkage estimators for the mean parameter matrix of a matrix variate elliptically contoured distribution. It is showed that the positive rule shrinkage estimator outperformed the shrinkage and unrestricted (maximum likelihood) estimators. To illustrate the findings of the paper, the relative risk functions for different degrees of freedoms are given for a multivariate t distribution. Shrinkage estimators for the matrix variate regression model under matrix normal, matrix t or Pearson VII error distributions would be special cases of this paper.

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