Archivio Istituzionale della RicercaWe 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
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http://hdl.handle.net/11583/1408962
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