Title :
Rank reduction and James-Stein estimation
Author :
Manton, Jonathan H. ; Hua, Yingbo
Author_Institution :
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
fDate :
11/1/1999 12:00:00 AM
Abstract :
This correspondence addresses the problem of estimating the signal in a signal-plus-Gaussian-noise model when it is known that the signal lies in a given subspace. An alternative to rank reduction is presented. The new estimator has the remarkable property of having a smaller mean-square error than that of the maximum-likelihood (also least-squares) estimator for all parameter values
Keywords :
Gaussian noise; estimation theory; mean square error methods; signal processing; James-Stein estimation; mean-square error; rank reduction; signal-plus-Gaussian-noise model; subspace; Australia Council; Gaussian noise; Information processing; Linear regression; Maximum likelihood estimation; Parameter estimation; Signal generators; Signal processing; State estimation; Vectors;
Journal_Title :
Signal Processing, IEEE Transactions on
