Statistical Methods for Stochastic Differential Equations

Mathieu Kessler

ISBN 10: 1439849404 / ISBN 13: 9781439849408

Published by Chapman And Hall/CRC Mai 2012, 2012

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 508 pp. Englisch.

Seller Inventory # 9781439849408

Bibliographic Details

• Title: Statistical Methods for Stochastic Differential Equations
• Publisher: Chapman And Hall/CRC Mai 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 1439849404
• ISBN 13: 9781439849408
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 32D millimeters 240 millimeters width by 161 millimeters height by 32 millimeters depth
• Weight: 922 grams

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