wavelet estimation of matrix-variate spherically distributions

In the shrinkage wavelet method, determining the right threshold is important, especially when estimating the location parameter for matrix-variate distributions is of interest. this paper presents a novel shrinkage wavelet estimator using soft thresholding, which is derived from stein s unbiased risk estimate (sure), for matrix-variate spherically distributed data. our goal is to find a class of the soft thresholding shrinkage wavelet estimator using a balanced loss function and evaluate its performance through simulation study.