We consider the problem of parameter estimation in a linear stochastic model, where the observations are affected by noise with uncertain variance. In particular, we discuss a linear estimator which minimizes a worst-case measure of the a-posteriori covariance of the parameters. The estimate is efficiently computed by means of convex programming, and may be updated with upcoming observations in a recursive setting

Minimum Variance Estimation with Uncertain Statistical Model / Calafiore, Giuseppe Carlo; EL GHAOUI, L.. - STAMPA. - 4:(2001), pp. 3497-3499. (Intervento presentato al convegno 40th Conference on Decision and Control tenutosi a Orlando, FL nel 04-07 Dec 2001) [10.1109/.2001.980400].

Minimum Variance Estimation with Uncertain Statistical Model

CALAFIORE, Giuseppe Carlo;
2001

Abstract

We consider the problem of parameter estimation in a linear stochastic model, where the observations are affected by noise with uncertain variance. In particular, we discuss a linear estimator which minimizes a worst-case measure of the a-posteriori covariance of the parameters. The estimate is efficiently computed by means of convex programming, and may be updated with upcoming observations in a recursive setting
2001
0780370619
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1408962
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