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The seventh volume in the SemStat series, Statistical Methods for
Stochastic Differential Equations presents current research trends
and recent developments in statistical methods for stochastic
differential equations. Written to be accessible to both new
students and seasoned researchers, each self-contained chapter
starts with introductions to the topic at hand and builds gradually
towards discussing recent research. The book covers Wiener-driven
equations as well as stochastic differential equations with jumps,
including continuous-time ARMA processes and COGARCH processes. It
presents a spectrum of estimation methods, including nonparametric
estimation as well as parametric estimation based on likelihood
methods, estimating functions, and simulation techniques. Two
chapters are devoted to high-frequency data. Multivariate models
are also considered, including partially observed systems,
asynchronous sampling, tests for simultaneous jumps, and multiscale
diffusions. Statistical Methods for Stochastic Differential
Equations is useful to the theoretical statistician and the
probabilist who works in or intends to work in the field, as well
as to the applied statistician or financial econometrician who
needs the methods to analyze biological or financial time series.
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