  The paper offers a novel unified approach to studying the accuracy of
parameter estimation by the quasi likelihood method. Important features of the
approach are: (1) The underlying model {is not assumed to be parametric}. (2)
No conditions on parameter identifiability are required. The parameter set can
be unbounded. (3) The model assumptions are quite general and there is no
specific structural assumptions like independence or weak dependence of
observations. The imposed conditions on the model are very mild and can be
easily checked in specific applications. (4) The established risk bounds are
{nonasymptotic} and valid for large, moderate and small samples. (5) The main
result is the concentration property of the quasi MLE giving an nonasymptotic
exponential bound for the probability that the considered estimate deviates out
of a small neighborhood of the "true" point.
  In standard situations under mild regularity conditions, the usual
consistency and rate results can be easily obtained as corollaries from the
established risk bounds. % The approach and the results are illustrated on the
example of generalized linear and single-index models.
