This paper deals with the robust estimation problem of a signal given noisy
observations. We assume that the actual statistics of the signal and
observations belong to a ball about the nominal statistics. This ball is formed
by placing a bound on the Tau-divergence family between the actual and the
nominal statistics. Then, the robust estimator is obtained by minimizing the
mean square error according to the least favorable statistics in that ball.
Therefore, we obtain a divergence family-based minimax approach to robust
estimation. We show in the case that the signal and observations have no
dynamics, the Bayes estimator is the optimal solution. Moreover, in the dynamic
case, the optimal offline estimator is the noncausal Wiener filter
