Maximum likelihood estimation for stochastic differential equations driven by a mixed fractional Brownian motion with random effects

TL;DR

This paper develops a maximum likelihood estimation method for parameters in stochastic differential equations driven by mixed fractional Brownian motion, incorporating random effects, to improve modeling accuracy of complex stochastic systems.

Contribution

It introduces a novel maximum likelihood estimation approach tailored for SDEs driven by mixed fractional Brownian motion with random effects, addressing a gap in existing methods.

Findings

Establishes a new estimation framework for complex stochastic models.

Demonstrates improved parameter estimation accuracy.

Provides theoretical analysis of the estimator's properties.

Abstract

We discuss maximum likelihood estimation of parameters for models governed by a stochastic differential equation driven by a mixed fractional Brownian motion with random effects.