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Statistical Methods for Stochastic Differential Equations

Mathieu Kessler | Alexander Lindner | Michael Sorensen
€137.99
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A01=Alexander Lindner
A01=Mathieu Kessler
A01=Michael Sorensen
advanced diffusion process estimation
Asymptotic Power Function
Asymptotic Scenario
asynchronous data analysis
Author_Alexander Lindner
Author_Mathieu Kessler
Author_Michael Sorensen
bridge
brownian
Burkholder Davis Gundy Inequality
Category=KCH
Category=PBKJ
Category=PBT
coefficients
Common Jumps
Compound Poisson Process
Conditional Expectations
density
diffusion
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Ergodic Diffusions
Estimating Function
Estimating functions for diffusion-type processes
Godambe Information
High Frequency Asymptotics
high frequency financial data
likelihood inference methods
Lim Supn
Local Martingale
Martingale Increments
MC
Microstructure Noise
Multipower Variations
multivariate stochastic processes
nonparametric estimation
Optimal Weight Matrix
Ornstein Uhlenbeck Process
Ornstein-Uhlenbeck related models driven by Levy processes
ornsteinuhlenbeck
Parameter estimation for multiscale diffusions: an overview
path
process
Quadratic Variation
sample
Skorokhod Topology
Stable Convergence
Statistics and high frequency data
Stochastic Differential Equation
Stochastic Integral
The econometrics of high frequency data
time series modeling
transition
Transition Density
Wiener Process

Product details

  • ISBN 9781439849408
  • Weight: 839g
  • Dimensions: 156 x 234mm
  • Publication Date: 17 May 2012
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

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 Alexander Lindner | Mathieu Kessler | Michael Sorensen

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

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