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.

中文翻译：

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双变量数据流的经验尾部依赖系数的空间效率估计

本文提出了一种空间有效的近似方法，用于不确定的二元数据流的经验尾部相关系数。具有流长度不变误差范围的近似值，利用了最近开发的用于双变量经验copula函数的摘要的工作。本文的工作准确地近似了每个边际分布的尾部中的二元经验语系，因此可以对数据流中观察到的两个变量之间的尾部相关性进行建模。在这些边缘尾部评估的Copulas可以用于估计尾部依赖系数。本文提出的对空间有效的双变量copula逼近的修改允许尾部相关系数的逼近误差保持流长不变。理论和数值上的证据，