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The modified permutation entropy-based independence test of time series

机译:基于改进的置换熵的时间序列独立性检验

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In time series, it is essential to check the independence of data by means of a proper method or an appropriate statistical test before any further analysis. Therefore, among different independence tests, a powerful and productive test has been introduced by Matilla-Garcia and Marin via m-dimensional vectorial process, in which the value of the process at time t includes m-histories of the primary process. However, this method causes a dependency for the vectors even when the independence assumption of random variables is considered. Considering this dependency, a modified test is obtained in this article through presenting a new asymptotic distribution based on weighted chi-square random variables. Also, some other alterations to the test have been made via bootstrap method and by controlling the overlap. Compared with the primary test, it is obtained that not only the modified test is more accurate but also, it possesses higher power.
机译:在时间序列中,在进行任何进一步分析之前,必须通过适当的方法或适当的统计检验来检查数据的独立性。因此,在不同的独立性测试中,Matilla-Garcia和Marin通过m维矢量过程引入了一种强大而富有成效的测试,其中在时间t的过程值包括主要过程的m历史。但是,即使考虑了随机变量的独立性假设,该方法也会对向量产生依赖性。考虑到这种依赖性,本文通过提出一种基于加权卡方随机变量的新渐近分布来获得改进的检验。此外,还通过引导方法和控制重叠对测试进行了其他更改。与初级测试相比,不仅改进测试更加准确,而且具有更高的功效。

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