Statistical Inference for Ergodic Diffusion Processes
encompasses a wealth of results from over ten years of mathematical
literature. It provides a comprehensive overview of existing
techniques, and presents - for the first time in book form - many
new techniques and approaches. An elementary introduction to the
field at the start of the book introduces a class of examples -
both non-standard and classical - that reappear as the
investigation progresses to illustrate the merits and demerits of
the procedures. The statements of the problems are in the spirit of
classical mathematical statistics, and special attention is paid to
asymptotically efficient procedures. Today, diffusion processes are
widely used in applied problems in fields such as physics,
mechanics and, in particular, financial mathematics. This book
provides a state-of-the-art reference that will prove invaluable to
researchers, and graduate and postgraduate students, in areas such
as financial mathematics, economics, physics, mechanics and the
biomedical sciences.
From the reviews:
"This book is very much in the Springer mould of graduate
mathematical statistics books, giving rapid access to the latest
literature...It presents a strong discussion of nonparametric and
semiparametric results, from both classical and Bayesian
standpoints...I have no doubt that it will come to be regarded as a
classic text." Journal of the Royal Statistical Society, Series A,
v. 167
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