wavelet thresholds for matrix-variate normal distribution under the reflected normal loss

The matrix-variate normal distribution is a probability distribution that is a generalization of the multivariate normal distribution to matrix-valued random variables. in this paper, we introduce a wavelet shrinkage estimator based on stein’s unbiased risk estimate (sure) threshold for matrix-variate normal distribution. we find a new sure threshold for soft thresholding wavelet shrinkage estimator under the reflected normal loss function in low dimensional cases. also, we obtain the restricted wavelet shrinkage estimator based on non-negative sub matrix of the mean matrix. finally, we present a simulation study to test the validity of the wavelet shrinkage.