TY - JOUR
T1 - Parameter estimation for fractional power type diffusion
T2 - A hybrid Bayesian-deep learning approach
AU - Araya, Héctor
AU - Plaza-Vega, Francisco
N1 - Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - ABC
KW - Parameter estimation
KW - fractional Brownian motion
KW - power-type fractional diffusion
UR - http://www.scopus.com/inward/record.url?scp=85178200138&partnerID=8YFLogxK
U2 - 10.1080/03610926.2023.2280522
DO - 10.1080/03610926.2023.2280522
M3 - Article
AN - SCOPUS:85178200138
SN - 0361-0926
VL - 53
SP - 8234
EP - 8254
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 22
ER -