Statistical Methods for Stochastic Differential Equations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Mathieu Kessler; Alexander Lindner; Michael Sorensen

ISBN 10: 1439849404 / ISBN 13: 9781439849408

Published by Chapman and Hall/CRC, 2012

About this Item

**INTERNATIONAL EDITION** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments go through via USPS/UPS/DHL with tracking numbers. Great professional textbook selling experience and expedite shipping service.

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Bibliographic Details

• Title: Statistical Methods for Stochastic Differential Equations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2012
• Language: English
• ISBN 10: 1439849404
• ISBN 13: 9781439849408
• Binding: Soft cover
• Edition: International Edition
• Condition: New
• Dust Jacket Condition: New

About this title

Synopsis

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.

About the Author

Matthieu Kessler, Department of Applied Mathematics and Statistics, University of Cartagena, Spain

Alexander Lindner, Institute of Mathematics and Statistics, TU Braunschweig, Germany

Michael Sorensen, Department of Mathematical Sciences, University of Copenhagen, Denmark