Minimum variance estimation with uncertain statistical model

We consider the problem of parameter estimation in a linear stochastic model, where the observations are affected by noise with uncertain variance. In particular, we discuss a linear estimator which minimizes a worst-case measure of the a-posteriori covariance of the parameters. The estimate is efficiently computed by means of convex programming, and may be updated with upcoming observations in a recursive setting