high dimensional wavelet parameter estimation for the matrix-variatenormal distribution

In this paper, we introduce a new soft-threshold wavelet shrinkage estimatorbased on the stein’s unbiased risk estimate (sure) for matrix-variate normaldistribution in a high dimensional case. we focus on particular thresholding rules toobtain a new sure threshold, and thus produce new estimators under quadratic lossfunction. finally, we present a simulation study to test the validity of the proposedestimator.