|
|
 |
|
Viewing report
|
|
|
|
Statistical Inference for Fractional Diffusion Processes
John Wiley and Sons Ltd, July 2010, Pages: 280
Stochastic processes are widely used for model building in the social, physical, engineering and life sciences as well as in financial economics. In model building, statistical inference for stochastic processes is of great importance from both a theoretical and an applications point of view.
This book deals with Fractional Diffusion Processes and statistical inference for such stochastic processes. The main focus of the book is to consider parametric and nonparametric inference problems for fractional diffusion processes when a complete path of the process over a finite interval is observable.
Key features:
- Introduces self-similar processes, fractional Brownian motion and stochastic integration with respect to fractional Brownian motion.
- Provides a comprehensive review of statistical inference for processes driven by fractional Brownian motion for modelling long range dependence.
- Presents a study of parametric and nonparametric inference problems for the fractional diffusion process.
- Discusses the fractional Brownian sheet and infinite dimensional fractional Brownian motion.
- Includes recent results and developments in the area of statistical inference of fractional diffusion processes.
Researchers and students working on the statistics of fractional diffusion processes and applied mathematicians and statisticians involved in stochastic process modelling will benefit from this book.
Product samples
A sample for this product is available. Please Login/Register to download this sample.
Customers who bought this item also bought
Multiscale Analysis of Complex Time Series : Integration of Chaos and Random Fractal Theory, and Beyond
High Temperature Plasmas: Theory and Mathematical Tools for Laser and Fusion Plasmas
Self-Similar Processes in Telecommunications
Graphics Processing Units in Computational Finance: Approaching the Mainstream (Strategic Focus)
Mathematical Methods in Survival Analysis, Reliability and Quality of Life
Monte Carlo Frameworks: Building Customisable High-performance C++ Applications
Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples
Multivariate Statistics: High-Dimensional and Large-Sample Approximations
Learning from Data: Concepts, Theory, and Methods, 2nd Edition
Nonstandard methods in stochastics. Edition No. 1
|
|
|
|
