2015
DOI: 10.1007/s12209-015-2461-5
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Analytical fitting functions of finite sample discrete entropies of white Gaussian noise

Abstract: : In order to find the convergence rate of finite sample discrete entropies of a white Gaussian noise(WGN), Brown entropy algorithm is numerically tested.With the increase of sample size, the curves of these finite sample discrete entropies are asymptotically close to their theoretical values.The confidence intervals of the sample Brown entropy are narrower than those of the sample discrete entropy calculated from its differential entropy, which is valid only in the case of a small sample size of WGN. The diff… Show more

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Cited by 3 publications
(2 citation statements)
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“…[27] In nonlinear time series analysis, the finite sample size will affect the estimation of entropy and some helpful conclusions can be found in Ref. [28]. In this study, to retain the information of signal as much as possible, the data points used in the following experiments are sufficient and the sampling frequency satisfies the Shannon sampling theorem.…”
Section: Pacsmentioning
confidence: 84%
“…[27] In nonlinear time series analysis, the finite sample size will affect the estimation of entropy and some helpful conclusions can be found in Ref. [28]. In this study, to retain the information of signal as much as possible, the data points used in the following experiments are sufficient and the sampling frequency satisfies the Shannon sampling theorem.…”
Section: Pacsmentioning
confidence: 84%
“…In addition, the influences of a moving window on prediction error and finite sample size on entropy estimation are the other two fundamental issues in nonlinear time series analyses. [15,16] The effect of these factors on SSA FuzzyEn should be further investigated. The robust singular value decomposition and the ensemble prediction model are potentially useful to tackle these issues in SSA FuzzyEn as well as other nonlinear time series approaches.…”
mentioning
confidence: 99%