DocumentCode
1253707
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
Volume
47
Issue
11
fYear
1999
fDate
11/1/1999 12:00:00 AM
Firstpage
3121
Lastpage
3125
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;
fLanguage
English
Journal_Title
Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
1053-587X
Type
jour
DOI
10.1109/78.796445
Filename
796445
Link To Document