Strong Consistency of the Bayesian Estimator for the Ornstein–Uhlenbeck Process

DOI10.1007/978-3-319-02069-3_19zbMath1407.62304OpenAlexW2181133353MaRDI QIDQ4561944

Kazuhiro Yasuda, Nicolas Vayatis, Arturo Kohatsu-Higa

Publication date: 13 December 2018

Published in: Inspired by Finance (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-319-02069-3_19

zbMATH Keywords

Ornstein-Uhlenbeck processparticle methodBayesian estimatorfiltering problemcomputational intensive parameter estimation

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15) Markov processes: estimation; hidden Markov models (62M05) Diffusion processes (60J60)

Related Items (2)

Strong Consistency of Bayesian Estimator Under Discrete Observations and Unknown Transition Density ⋮ An Ornstein-Uhlenbeck-Type Process Which Satisfies Sufficient Conditions for a Simulation-Based Filtering Procedure

Cites Work

• Parametric inference for discretely observed non-ergodic diffusions
• Fundamentals of stochastic filtering
• Estimation for diffusion processes from discrete observation
• Mixing: Properties and examples
• Estimators of diffusions with randomly spaced discrete observations: a general theory
• Approximation of the posterior density for diffusion processes
• On inference for partially observed nonlinear diffusion models using the Metropolis-Hastings algorithm
• Strong Consistency of Bayesian Estimator Under Discrete Observations and Unknown Transition Density
• Estimation of an Ergodic Diffusion from Discrete Observations
• Estimation for discretely observed diffusions using transform functions
• The Monte-Carlo method for filtering with discrete-time observations

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