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 Itô-Taylor approximation of the sample paths.
Dettaglio pubblicazione
2025, New Trends in Functional Statistics and Related Fields, Pages 159-168
Neural Drift Estimation for Ergodic Diffusions: Nonparametric Analysis and Numerical Exploration (04b Atto di convegno in volume)
Di Gregorio Simone, Iafrate Francesco
ISBN: 9783031923821; 9783031923838
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