Generally, books on mathematical statistics are restricted to the case
of independent identically distributed random variables. In this book
however, both this case AND the case of dependent variables, i.e.
statistics for discrete and continuous time processes, are studied. This
second case is very important for today's practitioners.
Mathematical Statistics and Stochastic Processes is based on decision
theory and asymptotic statistics and contains up-to-date information on
the relevant topics of theory of probability, estimation, confidence
intervals, non-parametric statistics and robustness, second-order
processes in discrete and continuous time and diffusion processes,
statistics for discrete and continuous time processes, statistical
prediction, and complements in probability.
This book is aimed at students studying courses on probability with an
emphasis on measure theory and for all practitioners who apply and use
statistics and probability on a daily basis.