Fractional Diffusion with Partial Observations
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Publication:2890081
DOI10.1080/03610926.2011.581173zbMath1315.62025OpenAlexW2038949648MaRDI QIDQ2890081
Alexandre Brouste, Alexandre Popier, Marina Kleptsyna
Publication date: 8 June 2012
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2011.581173
Asymptotic properties of parametric estimators (62F12) Optimal statistical designs (62K05) Fractional processes, including fractional Brownian motion (60G22) Non-Markovian processes: estimation (62M09) Diffusion processes (60J60)
Related Items (3)
A note on inference for the mixed fractional Ornstein-Uhlenbeck process with drift ⋮ Adaptative design for estimation of parameter of second order differential equation in fractional diffusion system ⋮ CONTROLLED DRIFT ESTIMATION IN FRACTIONAL DIFFUSION LINEAR SYSTEMS
Cites Work
- Design for estimation of the drift parameter in fractional diffusion systems
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- Asymptotic properties of MLE for partially observed fractional diffusion system with dependent noises
- Almost sure parameter estimation and convergence rates for hidden Markov models
- Maximum-likelihood estimation for hidden Markov models
- An elementary approach to a Girsanov formula and other analytical results on fractional Brownian motions
- Statistical inference for ergodic diffusion processes.
- Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
- Asymptotic normality of the maximum-likelihood estimator for general hidden Markov models
- Extension of the Kalman-Bucy filter to elementary linear systems with fractional Brownian noises
- Asymptotic normality of the maximum likelihood estimator in state space models
- Leroux's method for general hidden Markov models
- Optimal inputs for linear system identification
- Linear estimation of self-similar processes via Lamperti's transformation
- Statistical Inference for Probabilistic Functions of Finite State Markov Chains
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