Discrete-time inference for slow-fast systems driven by fractional Brownian motion
Statistics Theory
2021-03-26 v2 Dynamical Systems
Probability
Statistics Theory
Abstract
We study statistical inference for small-noise-perturbed multiscale dynamical systems where the slow motion is driven by fractional Brownian motion. We develop statistical estimators for both the Hurst index as well as a vector of unknown parameters in the model based on a single time series of observations from the slow process only. We prove that these estimators are both consistent and asymptotically normal as the amplitude of the perturbation and the time-scale separation parameter go to zero. Numerical simulations illustrate the theoretical results.
Cite
@article{arxiv.2007.11665,
title = {Discrete-time inference for slow-fast systems driven by fractional Brownian motion},
author = {Solesne Bourguin and Siragan Gailus and Konstantinos Spiliopoulos},
journal= {arXiv preprint arXiv:2007.11665},
year = {2021}
}
Comments
arXiv admin note: text overlap with arXiv:1906.02131