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Author

Nakahiro Yoshida

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Preprint Oct 2026

Network inference for SDEs with diverging dimension under small noise

We consider inference for network stochastic differential equations in a small-noise, high-dimensional regime, where the diffusion coefficient vanishes with $\ep\to0$ while the network size and parameter dimension may diverge. We develop a minimum-distance estimation framework based on a deterministic measurement model...

Francesco Iafrate, Nakahiro Yoshida, Stefano M. Iacus · 0 citations
Preprint Sep 2026

Risk comparison theorems and application to deep learning of diffusion coefficients

This work investigates the nonparametric estimation of the diffusion matrix in stochastic differential equations featuring multidimensional, strong mixing covariate processes and proposes a flexible statistical framework that does not require a linear basis representation.

A. Gloter, Nakahiro Yoshida · 0 citations

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