  This paper explores Maximum Likelihood in parametric models in the context of
Sanov type Large Deviation Probabilities. MLE in parametric models under
weighted sampling is shown to be associated with the minimization of a specific
divergence criterion defined with respect to the distribution of the weights.
Some properties of the resulting inferential procedure are presented; Bahadur
efficiency of tests are also considered in this context.
