Maximum Likelihood Estimation of Drift and Diffusion Functions
arXiv:physics/0611102 · doi:10.1016/j.physleta.2007.03.082
Abstract
The maximum likelihood approach is adapted to the problem of estimation of drift and diffusion functions of stochastic processes from measured time series. We reconcile a previously devised iterative procedure [Kleinhans et al., Physics Letters A (346), 2005] and put the application of the method on a firm theoretical basis.
5 pages, 2 figures
References in corpus (5)
- Analysis of data sets of stochastic systems
- On the proper reconstruction of complex dynamical systems spoilt by strong measurement noise
- An Iterative Procedure for the Estimation of Drift and Diffusion Coefficients of Langevin Processes
- Reconstruction of dynamical equations for traffic flow
- Improved estimation of Fokker-Planck equations through optimisation
Cited by in corpus (13)
- Estimation of Kramers-Moyal coefficients at low sampling rates
- Finite sampling interval effects in Kramers-Moyal analysis
- The Langevin Approach: An R Package for Modeling Markov Processes
- Estimation of drift and diffusion functions from time series data: A maximum likelihood framework
- Stochastic modelling of a noise driven global instability in a turbulent swirling jet
- Markov properties in presence of measurement noise
- Extended Kramers-Moyal analysis applied to optical trapping
- Non-parametric estimation of a Langevin model driven by correlated noise
- Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation
- Geometric and projection effects in Kramers-Moyal analysis
- Nonparametric model reconstruction for stochastic differential equation from discretely observed time-series data
- Continuous-Time Higher Order Markov Chains: Formulation and Parameter Estimation
- Parameter Estimation for Multivariate Diffusion Systems