Bayesian Approach to Hurst Exponent Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F17%3A10367928" target="_blank" >RIV/00216208:11150/17:10367928 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21340/17:00316536

Výsledek na webu

<a href="https://link.springer.com/article/10.1007%2Fs11009-017-9543-x" target="_blank" >https://link.springer.com/article/10.1007%2Fs11009-017-9543-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11009-017-9543-x" target="_blank" >10.1007/s11009-017-9543-x</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian Approach to Hurst Exponent Estimation

Popis výsledku v původním jazyce

Fractal investigation of a signal often involves estimating its fractal dimension or Hurst exponent H when considered as a sample of a fractional process. Fractional Gaussian noise (fGn) belongs to the family of self-similar fractional processes and it is dependent on parameter H. There are variety of traditional methods for Hurst exponent estimation. Our novel approach is based on zero-crossing principle and signal segmentation. Thanks to the Bayesian analysis, we present a new axiomatically based procedure of determining the expected value of Hurst exponent together with its standard deviation and credible intervals. The statistical characteristics are calculated at the interval level at first and then they are used for the deduction of the aggregate estimate. The methodology is subsequently used for the EEG signal analysis of patients suffering from Alzheimer disease.

Název v anglickém jazyce

Bayesian Approach to Hurst Exponent Estimation

Popis výsledku anglicky

Fractal investigation of a signal often involves estimating its fractal dimension or Hurst exponent H when considered as a sample of a fractional process. Fractional Gaussian noise (fGn) belongs to the family of self-similar fractional processes and it is dependent on parameter H. There are variety of traditional methods for Hurst exponent estimation. Our novel approach is based on zero-crossing principle and signal segmentation. Thanks to the Bayesian analysis, we present a new axiomatically based procedure of determining the expected value of Hurst exponent together with its standard deviation and credible intervals. The statistical characteristics are calculated at the interval level at first and then they are used for the deduction of the aggregate estimate. The methodology is subsequently used for the EEG signal analysis of patients suffering from Alzheimer disease.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Methodology and Computing in Applied Probability

ISSN

1387-5841

e-ISSN

—

Svazek periodika

19

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

973-983

Kód UT WoS článku

000407393600014

EID výsledku v databázi Scopus

2-s2.0-85009751131