Information-Theoretic Inequalities and Superefficient Estimates

It is proved that superefficient estimates for a matrix loss function do not exist in the class of estimates uniformly at least asymptotically efficient. An analogous result is established for sequential estimates. Integral asymptotic bounds for the estimation risks are also obtained for a large class of loss functions and a priori distribution functions.