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Space‐efficient estimation of empirical tail dependence coefficients for bivariate data streams

机译:生物数据流的经验尾依赖系数的空间高效估计

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This article proposes a space‐efficient approximation to empirical tail dependence coefficients of an indefinite bivariate stream of data. The approximation, which has stream‐length invariant error bounds, utilizes recent work on the development of a summary for bivariate empirical copula functions. The work in this paper accurately approximates a bivariate empirical copula in the tails of each marginal distribution, therefore modeling the tail dependence between the two variables observed in the data stream. Copulas evaluated at these marginal tails can be used to estimate the tail dependence coefficients. Modifications to the space‐efficient bivariate copula approximation, presented in this paper, allow the error of approximations to the tail dependence coefficients to remain stream‐length invariant. Theoretical and numerical evidence of this, including a case‐study using the Los Alamos National Laboratory netflow data‐set, is provided within this article.
机译:本文提出了空节空节空节空间尾依赖系数的无限二核数据流数据。具有流长不变错误限制的近似,利用最近的工作开发了一系列生物化实证卷曲功能。本文中的作品在每个边缘分布的尾部中精确地近似于双偏见的经验谱,因此在数据流中观察到的两个变量之间建模尾依赖性。在这些边缘尾部评估的Copulas可用于估计尾依赖系数。对本文呈现的空节空节空节的分谱近似的修改允许对尾依赖系数的近似误差保持流长度不变。本文在本文中提供了使用LOS Alamos National实验室Netflow数据集的理论和数值证据。

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