Estimation for nonlinear stochastic differential equations by a local linearization method1
From MaRDI portal
Publication:4208316
DOI10.1080/07362999808809559zbMath0912.60078OpenAlexW2026374957MaRDI QIDQ4208316
Publication date: 18 May 1999
Published in: Stochastic Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/07362999808809559
Related Items (30)
Systematic physics constrained parameter estimation of stochastic differential equations ⋮ Approximation of continuous time stochastic processes by the local linearization method revisited ⋮ Stochastic continuous time growth models that allow for closed form solutions ⋮ Optimal control problem with an integral equation as the control object ⋮ A Study of the Efficiency of Exact Methods for Diffusion Simulation ⋮ Generalized dissipative state estimation for discrete-time nonhomogeneous semi-Markov jump nonlinear systems ⋮ Modeling the euglycemic hyperinsulinemic clamp by stochastic differential equations ⋮ Specification tests for univariate diffusions ⋮ Approximate minimum Hellinger distance estimation for diffusion processes using Euler's scheme ⋮ Estimation of nonlinear mixed‐effects continuous‐time models using the continuous‐discrete extended Kalman filter ⋮ Stability of partially implicit Langevin schemes and their MCMC variants ⋮ Weak local linear discretizations for stochastic differential equations: convergence and numerical schemes ⋮ On local linear approximations to diffusion processes ⋮ Efficient importance sampling maximum likelihood estimation of stochastic differential equations ⋮ The local linearization scheme for nonlinear diffusion models with discontinuous coefficients ⋮ Maximum likelihood estimation of partially observed diffusion models ⋮ Convergence rate of weak local linearization schemes for stochastic differential equations with additive noise ⋮ Approximation of continuous time stochastic processes by a local linearization method ⋮ Quasi‐maximum likelihood estimation of discretely observed diffusions ⋮ A quasi-maximum likelihood method for estimating the parameters of multivariate diffusions ⋮ Parameter estimation in stochastic differential equations with Markov chain Monte Carlo and non-linear Kalman filtering ⋮ Realistically Coupled Neural Mass Models Can Generate EEG Rhythms ⋮ Closed-form likelihoods for stochastic differential equation growth models ⋮ Transition Density and Simulated Likelihood Estimation for Time-Inhomogeneous Diffusions ⋮ Estimation of a CIR process with jumps using a closed form approximation likelihood under a strong approximation of order 1 ⋮ Weak Local Linear Discretizations for Stochastic Differential Equations with Jumps ⋮ Approximation of transition densities of stochastic differential equations by saddlepoint methods applied to small-time Ito-Taylor sample-path expansions ⋮ Quantifying uncertainty with a derivative tracking SDE model and application to wind power forecast data ⋮ Local Linear Approximations of Jump Diffusion Processes ⋮ A multifactor transformed diffusion model with applications to VIX and VIX futures
Cites Work
- Continuous Time Series Models for Unequally Spaced Data Applied to Modeling Atomic Clocks
- On the parameters estimation of continuous-time ARMA processes from noisy observations
- Numerical Treatment of Stochastic Differential Equations
- A local linearization approach to nonlinear filtering
- Statistical Identification of Nonlinear Random Vibration Systems
- CONTINUOUS-TIME DYNAMICAL SYSTEMS WITH SAMPLED DATA, ERRORS OF MEASUREMENT AND UNOBSERVED COMPONENTS
This page was built for publication: Estimation for nonlinear stochastic differential equations by a local linearization method1