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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

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.
ISBN: 9783031923821; 9783031923838
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