Asymptotic Theory of Statistical Inference

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. The book 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 non-linear regression, and estimators of parameters for stable laws are discussed 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.