Asymptotic theory of statistical inference

the long version

An up-to-date and concise description of recent results in probability theory and stochastic processes useful in the study of asymptotic theory of statistical inference. Brings together new material on the interplay between recent advances in probability theory and their applications to the asymptotic theory of statistical inference. Asymptotic theory of maximum likelihood and Bayes estimation, asymptotic properties of least squares estimators in nonlinear regression, and estimators of parameters for stable laws are dicussed from the point of view of stochastic processes. This leads to better results than the Taylor expansions approach used in the classical theory of maximum likelihood estimation.