Matrix analysis
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The SVD and reduced rank signal processing
Signal Processing - Theme issue on singular value decomposition
Time series: data analysis and theory
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Optimization by Vector Space Methods
Optimization by Vector Space Methods
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Introduction to Space-Time Wireless Communications
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Linear Models and Generalizations: Least Squares and Alternatives
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IEEE Transactions on Signal Processing - Part II
On the complexity of sphere decoding in digital communications
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A competitive minimax approach to robust estimation of random parameters
IEEE Transactions on Signal Processing
Reduced-rank channel estimation for time-slotted mobile communication systems
IEEE Transactions on Signal Processing
Pilot-assisted channel estimation based on second-order statistics
IEEE Transactions on Signal Processing
Relative Karhunen-Loeve transform
IEEE Transactions on Signal Processing
Data adaptive rank-shaping methods for solving least squaresproblems
IEEE Transactions on Signal Processing
Minimum variance linear receivers for multiaccess MIMO wireless systems with space-time block coding
IEEE Transactions on Signal Processing
Wiener filters in canonical coordinates for transform coding,filtering, and quantizing
IEEE Transactions on Signal Processing
Maximum likelihood parameter and rank estimation in reduced-rankmultivariate linear regressions
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Optimal reduced-rank estimation and filtering
IEEE Transactions on Signal Processing
On the relative error probabilities of linear multiuser detectors
IEEE Transactions on Information Theory
Group-blind multiuser detection for uplink CDMA
IEEE Journal on Selected Areas in Communications
Robust multiuser detection for multicarrier CDMA systems
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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This paper proposes a novel linear estimator named stochastic MV-PURE estimator, developed for the stochastic linear model, and designed to provide improved performance over the linear minimum mean square error (MMSE) Wiener estimator in cases prevailing in practical, real-world settings, where at least some of the second-order statistics of the random vectors under consideration are only imperfectly known. The proposed estimator shares its main mathematical idea and terminology with the recently introduced minimum-variance pseudounbiased reduced-rank estimator (MV-PURE), developed for the linear regression model. The proposed stochastic MV-PURE estimator minimizes the mean square error (MSE) of its estimates subject to rank constraint and inducing minimium distortion to the target random vector. Therefore, the stochastic MV-PURE combines the techniques of the reduced rank Wiener filter (named in this paper RR-MMSE) and the distortionless-constrained estimator (named in this paper C-MMSE), in order to achieve greater robustness against noise or model errors than RR-MMSE and C-MMSE. Furthermore, to ensure that the stochastic MV-PURE estimator combines the reduced-rank and minimum-distortion approaches in the MSE-optimal way, we propose a rank selection criterion which minimizes the MSE of the estimates obtained by the stochastic MV-PURE. As a numerical example, we employ the stochastic MV-PURE, RR-MMSE, C-MMSE, and MMSE estimators as linear receivers in a MIMO wireless communication system. This example is chosen as a typiical signal processing scenario, where the statistical information on the data, on which the estimates are built, is only imperfectly known. We verify that the stochastic MV-PURE achieves the lowest MSE and symbol error rate (SER) in such settings by employing the proposed rank selection criterion.