Bayesian Approach to Hurst Exponent Estimation
The result's identifiers
Result code in 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>
Alternative codes found
RIV/68407700:21340/17:00316536
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Bayesian Approach to Hurst Exponent Estimation
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Methodology and Computing in Applied Probability
ISSN
1387-5841
e-ISSN
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Volume of the periodical
19
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
11
Pages from-to
973-983
UT code for WoS article
000407393600014
EID of the result in the Scopus database
2-s2.0-85009751131