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Visual information and expert’s idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation

An approximation of the fractional Brownian motion based on the Ornstein-Uhlenbeck process is used to obtain an asymptotic likelihood function. Two estimators of the Hurst index are then presented in the likelihood approach. The first estimator is produced according to the observed values of the sam...

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Detalles Bibliográficos
Autores principales: Taheriyoun, Ali R., Moghimbeygi, Meisam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5307349/
https://www.ncbi.nlm.nih.gov/pubmed/28195153
http://dx.doi.org/10.1038/srep42482
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author Taheriyoun, Ali R.
Moghimbeygi, Meisam
author_facet Taheriyoun, Ali R.
Moghimbeygi, Meisam
author_sort Taheriyoun, Ali R.
collection PubMed
description An approximation of the fractional Brownian motion based on the Ornstein-Uhlenbeck process is used to obtain an asymptotic likelihood function. Two estimators of the Hurst index are then presented in the likelihood approach. The first estimator is produced according to the observed values of the sample path; while the second one employs the likelihood function of the incremental process. We also employ visual roughness of realization to restrict the parameter space and to obtain prior information in Bayesian approach. The methods are then compared with three contemporary estimators and an experimental data set is studied.
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spelling pubmed-53073492017-02-22 Visual information and expert’s idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation Taheriyoun, Ali R. Moghimbeygi, Meisam Sci Rep Article An approximation of the fractional Brownian motion based on the Ornstein-Uhlenbeck process is used to obtain an asymptotic likelihood function. Two estimators of the Hurst index are then presented in the likelihood approach. The first estimator is produced according to the observed values of the sample path; while the second one employs the likelihood function of the incremental process. We also employ visual roughness of realization to restrict the parameter space and to obtain prior information in Bayesian approach. The methods are then compared with three contemporary estimators and an experimental data set is studied. Nature Publishing Group 2017-02-14 /pmc/articles/PMC5307349/ /pubmed/28195153 http://dx.doi.org/10.1038/srep42482 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Taheriyoun, Ali R.
Moghimbeygi, Meisam
Visual information and expert’s idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation
title Visual information and expert’s idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation
title_full Visual information and expert’s idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation
title_fullStr Visual information and expert’s idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation
title_full_unstemmed Visual information and expert’s idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation
title_short Visual information and expert’s idea in Hurst index estimation of the fractional Brownian motion using a diffusion type approximation
title_sort visual information and expert’s idea in hurst index estimation of the fractional brownian motion using a diffusion type approximation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5307349/
https://www.ncbi.nlm.nih.gov/pubmed/28195153
http://dx.doi.org/10.1038/srep42482
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