论文标题
在未知形式的丢失数据下使用Kendall的$τ$测试依赖的不可能。
The Impossibility of Testing for Dependence Using Kendall's $τ$ Under Missing Data of Unknown Form
论文作者
论文摘要
本文讨论了在丢失的数据问题的背景下，使用Kendall的$τ$在两个连续随机变量之间测试依赖性的统计推断问题。我们证明，针对这种关联度量的最差案例集合始终包括零。该结果的结果是，使用Kendall的$τ$对依赖性的鲁棒性推断是不可能的，在肯德尔的$τ$相对于产生失踪过程的形式方面是不可能的。
This paper discusses the statistical inference problem associated with testing for dependence between two continuous random variables using Kendall's $τ$ in the context of the missing data problem. We prove the worst-case identified set for this measure of association always includes zero. The consequence of this result is that robust inference for dependence using Kendall's $τ$, where robustness is with respect to the form of the missingness-generating process, is impossible.
