Parameter estimation for diffusion process from perturbed discrete observations
From MaRDI portal
Publication:6116461
DOI10.1080/03610918.2020.1871014OpenAlexW3119414673MaRDI QIDQ6116461
Unnamed Author, Dang Duc Trong
Publication date: 18 July 2023
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2020.1871014
Asymptotic properties of parametric estimators (62F12) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Strong consistency and rates for deconvolution of multivariate densities of stationary processes
- Estimation of diffusion parameters for discretely observed diffusion processes
- Deconvolution problems in nonparametric statistics
- Basic properties of strong mixing conditions. A survey and some open questions
- Estimation for diffusion processes from discrete observation
- Asymptotic normality for deconvolution estimators of multivariate densities of stationary processes
- Minimum Hellinger distance estimates for parametric models
- Nonparametric deconvolution problem for dependent sequences
- Simulation and inference for stochastic differential equations. With R examples.
- Parameter estimation of one-dimensional diffusion process by minimum Hellinger distance method
- A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
- Some Limit Theorems for Random Functions. I
- Multivariate probability density deconvolution for stationary random processes
- On the estimation of nonlinear time series models
- Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach
- Stochastic volatility models as hidden Markov models and statistical applications
