Parameter estimation for fractional power type diffusion: A hybrid Bayesian-deep learning approach

Araya H.; Plaza-Vega, F.

Keywords: parameter estimation, abc, fractional brownian motion, power-type fractional diffusion

Abstract

Abstract.: In this article, we consider the problem of parameter estimation in a power-type diffusion driven by fractional Brownian motion with Hurst parameter in (Formula presented.). To estimate the parameters of the process, we use an approximate bayesian computation method. Also, a particular case is addressed by means of variations and wavelet-type methods. Several theoretical properties of the process are studied and numerical examples are provided in order to show the small sample behavior of the proposed methods.

Más información

Título según WOS: Parameter estimation for fractional power type diffusion: A hybrid Bayesian-deep learning approach
Título según SCOPUS: Parameter estimation for fractional power type diffusion: A hybrid Bayesian-deep learning approach
Título de la Revista: Communications in Statistics - Theory and Methods
Volumen: 53
Número: 22
Editorial: Taylor and Francis Inc.
Fecha de publicación: 2024
Página final: 8254
Idioma: English
DOI:

10.1080/03610926.2023.2280522

Notas: ISI, SCOPUS