We address the problem of estimating an unknown parameter vector x in a linear model y = Cx+v subject to the a priori information that the true parameter vector x belongs to a known convex polytope . The proposed estimator has the parametrized structure of the maximum a posteriori probability (MAP) estimator with prior Gaussian distribution, whose mean and covariance parameters are suitably designed via a linear matrix inequality approach so as to guarantee, for any x , an improvement of the mean-squared error (MSE) matrix over the least-squares (LS) estimator. It is shown that this approach outperforms existing “superefficient” estimators for constrained parameters based on different parametrized structures and/or shapes of the parameter membership region.

Estimation of constrained parameters with guaranteed MSE improvement / A. BENAVOLI; L. CHISCI; A. FARINA. - In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - ISSN 1053-587X. - STAMPA. - 55:(2007), pp. 1264-1274. [10.1109/TSP.2006.888094]

Estimation of constrained parameters with guaranteed MSE improvement

CHISCI, LUIGI;
2007

Abstract

We address the problem of estimating an unknown parameter vector x in a linear model y = Cx+v subject to the a priori information that the true parameter vector x belongs to a known convex polytope . The proposed estimator has the parametrized structure of the maximum a posteriori probability (MAP) estimator with prior Gaussian distribution, whose mean and covariance parameters are suitably designed via a linear matrix inequality approach so as to guarantee, for any x , an improvement of the mean-squared error (MSE) matrix over the least-squares (LS) estimator. It is shown that this approach outperforms existing “superefficient” estimators for constrained parameters based on different parametrized structures and/or shapes of the parameter membership region.
2007
55
1264
1274
A. BENAVOLI; L. CHISCI; A. FARINA
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/204095
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