Bayesian estimation of self-similarity exponent

Bayesian estimation of self-similarity exponent
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自相似指数的贝叶斯估计

DOI:
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发表时间:
2011
期刊:
Physical review. E, Statistical, nonlinear, and soft matter physics
影响因子:
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通讯作者:
N. Makarava
N. Makarava
中科院分区:
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文献类型:
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作者:
N. Makarava

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In this study we propose a bayesian approach to the estimation of the Hurst exponent in terms of linear mixed models. Even for unevenly sampled signals and signals with gaps, our method is applicable. We test our method by using artificial fractional brownian motion of different length and compare it with the detrended fluctuation analysis technique. The estimation of the Hurst exponent of a Rosenblatt process is shown as an example of an H-self-similar process with non-gaussian dimensional distribution. Additionally, we perform an analysis with real data, the Dow-Jones Industrial Average closing values, and analyze its temporal variation of the Hurst exponent.
DOI: 10.1209/0295-5075/100/40003
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