| Multiscale Entropy under the Inverse Gaussian Distribution： Analytical Results |
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| 引用本文： | 唐莹,裴文江,夏海山,何振亚.Multiscale Entropy under the Inverse Gaussian Distribution： Analytical Results[J].中国物理快报,2007,24(6):1490-1493. |
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| 作者姓名： | 唐莹 裴文江 夏海山 何振亚 |
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| 作者单位： | Department of Radio Engineering, Southeast University, Nanjing 210096 |
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| 基金项目： | Supported by the Natural Science Foundation of China under Grant 60672095, the National Information Security Program of China Grant 2005A14, and the National High Technology Project of China under Grant 2002AA143010 and 2003AA143040. |
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| 摘 要： | The multiscale entropy （MSE） reveals the intrinsic multiple scales in the complexity of physical and physiological signals, which are usually featured by heavy-tailed distributions. However, most research results are pure experimental search. Recently, Costa et al. have made the first attempt to present the theoretical basis of MSE, but it only supports the Gaussian distribution Phys Rev. E 71 （2005） 021906]. We present the theoretical basis of MSE under the inverse Gaussian distribution, a typical model for physiological, physical and financial data sets. The analysis allows for uncorrelated inverse Gaussian process and 1/f noise with the multivariate inverse Gaussian distribution, and then provides a reliable foundation for the potential applications of MSE to explore complev nhwical and Dhwical time series.
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| 关 键 词： | 熵 逆 高斯力学 物理学 |
| 收稿时间： | 2006-10-23 |
| 修稿时间： | 2006-10-23 |
Multiscale Entropy under the Inverse Gaussian Distribution: Analytical Results |
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| TANG Ying,PEI Wen-Jiang,XIA Hai-Shan,HE Zhen-Ya.Multiscale Entropy under the Inverse Gaussian Distribution: Analytical Results[J].Chinese Physics Letters,2007,24(6):1490-1493. |
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| Authors: | TANG Ying PEI Wen-Jiang XIA Hai-Shan HE Zhen-Ya |
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| Affiliation: | Department of Radio Engineering, Southeast University, Nanjing 210096 |
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| Abstract: | The multiscale entropy (MSE) reveals the intrinsic multiple scales in the complexity of physical and physiological signals, which are usually featured by heavy-tailed distributions. However, most research results are pure experimental search. Recently, Costa et al. have made the first attempt to present the theoretical basis of MSE, but it only supports the Gaussian distribution Phys Rev. E 71 (2005) 021906]. We present the theoretical basis of MSE under the inverse Gaussian distribution, a typical model for physiological, physical and financial data sets. The analysis allows for ncorrelated inverse Gaussian process and 1/f noise with the multivariateinverse Gaussian distribution, and then provides a reliable foundation for the potential applications of MSE to explore complex physical and physical time series. |
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| Keywords: | 05 40 Ca 05 45 Tp 65 40 Gr |
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