On asymptotically optimal estimates for general observations

Asymptotically maximum likelihood estimators and estimators asymptotically minimizing criterial functions of observations are considered in statistical models with generalized sequences of observations. New necessary and sufficient conditions for consistency of these estimators are established. The applicability of these conditions is illustrated on regression models with Gaussian and contaminated observations and on models of exponentially distributed random processes and fields.