A robust nonparametric framework for reconstruction of stochastic differential equation models

DOI10.1016/j.physa.2016.01.016zbMath1400.60083OpenAlexW2234596001WikidataQ115341759 ScholiaQ115341759MaRDI QIDQ1619315

Yalda Rajabzadeh, Hamidreza Amindavar, Amir Hossein Rezaie

Publication date: 13 November 2018

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2016.01.016

zbMATH Keywords

stochastic differential equationparameter estimationstationary processnonparametric method

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10)

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Error bounds of the invariant statistics in machine learning of ergodic Itô diffusions ⋮ Varying-coefficient stochastic differential equations with applications in ecology

Uses Software

• sde

Cites Work

• Kernel-based regression of drift and diffusion coefficients of stochastic processes
• Higher-order implicit strong numerical schemes for stochastic differential equations
• A note on estimating drift and diffusion parameters from time series
• Nonparametric and semiparametric models.
• Support-vector networks
• Experimental indications for Markov properties of small-scale turbulence
• Evidence of Markov properties of high frequency exchange rate data
• Stochastic differential equations. An introduction with applications.

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