  We establish some new non-asymptotical lower bounds for deviation of regular
unbiased estimation of unknown parameter from its true value in different
norms, alike the classical Rao-Kramer's inequality.
  We show that if the new norm is weaker that ordinary Hilbertian norm, that
the rate of convergence of arbitrary regular unbiased estimate does not exceed
$ 1/\sqrt{n}, $ and if the new norm is stronger that one, the rate of
convergence of the well-known Maximal Likelihood Estimate (MLE) is also equal
to $ 1/\sqrt{n}.
