Abstract
This paper addresses the problem of maximum likelihood parameter estimation in linear models affected by Gaussian noise, whose mean and covariance matrix are uncertain. The proposed estimate maximizes a lower bound on the worst-case (with respect to the uncertainty) likelihood of the measured sample, and is computed solving a semidefinite optimization problem (SDP). The problem of linear robust estimation is also studied in the paper, and the statistical and optimality properties of the resulting linear estimator are discussed.
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