Bayesian Approach to Hurst Exponent Estimation
Abstract 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...
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Gespeichert in:
| Autor*in: |
Dlask, Martin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Methodology and computing in applied probability - Springer US, 1999, 19(2017), 3 vom: 18. Jan., Seite 973-983 |
|---|---|
| Übergeordnetes Werk: |
volume:19 ; year:2017 ; number:3 ; day:18 ; month:01 ; pages:973-983 |
| Links: |
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| DOI / URN: |
10.1007/s11009-017-9543-x |
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| 520 | |a Abstract 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. | ||
| 650 | 4 | |a Fractal dimension | |
| 650 | 4 | |a Hurst exponent | |
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| 650 | 4 | |a Alzheimer disease | |
| 700 | 1 | |a Kukal, Jaromir |4 aut | |
| 700 | 1 | |a Vysata, Oldrich |4 aut | |
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Bayesian Approach to Hurst Exponent Estimation |
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Abstract 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. © Springer Science+Business Media New York 2017 |
| abstractGer |
Abstract 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. © Springer Science+Business Media New York 2017 |
| abstract_unstemmed |
Abstract 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. © Springer Science+Business Media New York 2017 |
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| title_short |
Bayesian Approach to Hurst Exponent Estimation |
| url |
https://doi.org/10.1007/s11009-017-9543-x |
| remote_bool |
false |
| author2 |
Kukal, Jaromir Vysata, Oldrich |
| author2Str |
Kukal, Jaromir Vysata, Oldrich |
| ppnlink |
335482279 |
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| doi_str |
10.1007/s11009-017-9543-x |
| up_date |
2024-07-04T00:38:41.554Z |
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1803606841418055680 |
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