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arXiv · 2505.24383

Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration

Simone Di Gregorio·Francesco Iafrate
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Abstract

We take into consideration generalization bounds for the problem of the estimation of the drift component for ergodic stochastic differential equations, when the estimator is a ReLU neural network and the estimation is non-parametric with respect to the statistical model. We show a practical way to enforce the theoretical estimation procedure, enabling inference on noisy and rough functional data. Results are shown for a simulated It\^o-Taylor approximation of the sample paths.

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Simone Di Gregorio, Francesco Iafrate. 2025-05-30. Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration. https://doi.org/10.1007/978-3-031-92383-8_20

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