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Filtered data based estimators for stochastic processes driven by colored noise

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  • Pavliotis, Grigorios A.
  • Reich, Sebastian
  • Zanoni, Andrea

Abstract

We consider the problem of estimating unknown parameters in stochastic differential equations driven by colored noise, which we model as a sequence of Gaussian stationary processes with decreasing correlation time. We aim to infer parameters in the limit equation, driven by white noise, given observations of the colored noise dynamics. We consider both the maximum likelihood and the stochastic gradient descent in continuous time estimators, and we propose to modify them by including filtered data. We provide a convergence analysis for our estimators showing their asymptotic unbiasedness in a general setting and asymptotic normality under a simplified scenario.

Suggested Citation

  • Pavliotis, Grigorios A. & Reich, Sebastian & Zanoni, Andrea, 2025. "Filtered data based estimators for stochastic processes driven by colored noise," Stochastic Processes and their Applications, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:spapps:v:181:y:2025:i:c:s0304414924002667
    DOI: 10.1016/j.spa.2024.104558
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    References listed on IDEAS

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