Recursive estimation for stochastic damping hamiltonian systems

DOI10.1080/10485252.2015.1046451zbMath1326.62083OpenAlexW1527686561MaRDI QIDQ3455255

Patrick Cattiaux, Clémentine Prieur, José Rafael León

Publication date: 4 December 2015

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2015.1046451

zbMATH Keywords

nonparametric estimationrecursive estimationpartial observationshypoelliptic diffusion

Mathematics Subject Classification ID

Density estimation (62G07) Central limit and other weak theorems (60F05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10)

Related Items (1)

An overlook on statistical inference issues for stochastic damping hamiltonian systems under the fluctuation-dissipation condition

Cites Work

• Nonparametric estimation of conditional probability densities and expectations of stationary processes: Strong consistency and rates
• Explicit parametrix and local limit theorems for some degenerate diffusion processes
• Asymptotic normality of recursive density estimates under some dependence assumptions
• Strong consistency and rates for recursive probability density estimators of stationary processes
• Almost sure convergence of recursive density estimators for stationary mixing processes
• Remarks on some recursive estimators of a probability density
• Large and moderate deviations and exponential convergence for stochastic damping Hamiltonian systems.
• Rate of convergence for ergodic continuous Markov processes: Lyapunov versus Poincaré
• Estimation for stochastic damping Hamiltonian systems under partial observation. I: Invariant density
• Recursive probability density estimation for weakly dependent stationary processes
• Dependent Lindeberg central limit theorem and some applications

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