Parametric estimation of stationary stochastic processes under indirect observability

DOI10.1007/s10955-011-0253-4zbMath1225.82044OpenAlexW1988886356MaRDI QIDQ637529

Robert Azencott, Ilya Timofeyev, Arjun Beri

Publication date: 6 September 2011

Published in: Journal of Statistical Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10955-011-0253-4

zbMATH Keywords

Gaussian processesadaptive sub-samplingempirical covariance estimatorsindirect observabilitynon vanishing lags

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Stochastic methods (Fokker-Planck, Langevin, etc.) applied to problems in time-dependent statistical mechanics (82C31)

Related Items (4)

Parametric estimation from approximate data: non-Gaussian diffusions ⋮ A new framework for extracting coarse-grained models from time series with multiscale structure ⋮ Perturbation-based inference for diffusion processes: Obtaining effective models from multiscale data ⋮ Realised volatility and parametric estimation of Heston SDEs

Cites Work

• Adaptive sub-sampling for parametric estimation of Gaussian diffusions
• Maximum likelihood drift estimation for multiscale diffusions
• A priori tests of a stochastic mode reduction strategy
• Parameter estimation for multiscale diffusions
• Diffusion Estimation from Multiscale Data by Operator Eigenpairs
• Reduction of deterministic coupled atmosphere–ocean models to stochastic ocean models: a numerical case study of the Lorenz–Maas system
• A Tale of Two Time Scales
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