Rank reduction and James-Stein estimation

This correspondence addresses the problem of estimating the signal in a sig nal-plus-Gaussian-noise model when it is known that the signal lies in a gi ven subspace. An alternative to rank reduction is presented. The new estima tor has the remarkable property of having a smaller mean-square error than that of the maximum-likelihood (also least-squares) estimator for all param eter values.