Worst-Case Maximum Likelyhood Estimation in the Linear Model

This paper addresses the problem of maximum likelyhood parameter estimation in linear models aaected by structured deterministic uncertainty in the regression matrix and random gaussian noise. The proposed estimator maximizes the worst-case (with respect to the deterministic uncertainty) likelyhood of the measured sample. The estimate is computed solving a semideenite optimization problem (SDP). In the particular case of unstructured uncertainty, this SDP simply requires minimization of a scalar convex function of one variable.

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