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.

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