Sub-sampling in Parametric Estimation of Effective Stochastic Models =20 from Discrete Data

Ilya Timofeyev
University of Houston
Department of Mathematics

It is often desirable to derive an effective stochastic model for the physical process from observational and/or numerical data. Various techniques exist for performing estimation of drift and diffusion in stochastic differential equations from discrete datasets.
In this talk we discuss the question of sub-sampling of the data when it is desirable to approximate statistical features of a smooth chaotic trajectory by a stochastic differential equation. In this case estimation of stochastic differential equations would yield incorrect results if the dataset is too dense in time. Therefore, the dataset has to sub-sampled (i.e. rarefied) to ensure estimators' =20 consistency.

Presentation (PDF File)

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