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A Regularized Estimator For Linear Regression Model With Possibly Singular Covariance

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2 Author(s)
Hong Son Hoang ; SHOM/CMO, Toulouse, France ; Rémy Baraille

A regularized estimator is proposed for regression models in the case where the covariances may be singular. Conditions guaranteeing proximity of a regularized estimator to the optimal estimator are obtained by appropriate choice of regularization parameters by allowing a prescribed level of uncertainty. A simple Monte-Carlo simulation study is reported to highlight some aspects and performance of the proposed approach.

Published in:

IEEE Transactions on Automatic Control  (Volume:58 ,  Issue: 1 )