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

Héctor Araya, Francisco Plaza-Vega

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

Resumen

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.

Idioma originalInglés
Páginas (desde-hasta)8234-8254
Número de páginas21
PublicaciónCommunications in Statistics - Theory and Methods
Volumen53
N.º22
DOI
EstadoPublicada - 2024
Publicado de forma externa

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