An estimation of Fisher information bound for distribution-dependent SDEs driven by fractional Brownian motion with small noise

TL;DR

This paper investigates the Fisher information convergence rate in the central limit theorem for distribution-dependent SDEs driven by fractional Brownian motion with small noise, establishing the optimal order of convergence.

Contribution

It provides the first analysis of Fisher information bounds for distribution-dependent SDEs driven by fractional Brownian motion, demonstrating the optimal convergence rate.

Findings

Fisher information convergence rate is of optimal order.

Established bounds for distribution-dependent SDEs driven by fractional Brownian motion.

Enhanced understanding of small noise effects on Fisher information in complex stochastic systems.

Abstract

In this paper, we consider the distribution-dependent SDE driven by fractional Brownian motion with small noise and study the rate of Fisher information convergence in the central limit theorem for the solution of SDE, then we show that the convergence rate is of optimal order.