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

• URL: http://arxiv.org/abs/2505.24383v1
• Date: Fri, 30 May 2025 09:12:49 GMT
• Title: Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration
• Authors: Simone Di Gregorio, Francesco Iafrate,
• Abstract summary: We show a practical way to enforce the theoretical estimation procedure, enabling inference on noisy and rough functional data.<n>Results are shown for a simulated Ito-Taylor approximation of the sample paths.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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