Robust maximum likelihood estimation in the linear model

✍ Scribed by Giuseppe Calafiore; Laurent El Ghaoui

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 181 KB
• Volume: 37
• Category: Article
• ISSN: 0005-1098
• DOI: 10.1016/s0005-1098(00)00189-8

✦ Synopsis

This paper addresses the problem of maximum likelihood parameter estimation in linear models a!ected 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 semide"nite 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.