当前位置: X-MOL 学术arXiv.math.ST › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Kendall's tau estimator for bivariate zero-inflated count data
arXiv - MATH - Statistics Theory Pub Date : 2022-08-05 , DOI: arxiv-2208.03155
Elisa Perrone, Edwin R. van den Heuvel, Zhuozhao Zhan

In this paper, we extend the work of Pimentel et al. (2015) and propose an adjusted estimator of Kendall's $\tau$ for bivariate zero-inflated count data. We provide achievable lower and upper bounds of our proposed estimator and show its relationship with current literature. In addition, we also suggest an estimator of the achievable bounds, thereby helping practitioners interpret the results while working with real data. The performance of the proposed estimator for Kendall's $\tau$ is unbiased with smaller mean squared errors compared to the unadjusted estimator of Pimentel et al. (2015). Our results also show that the bound estimator can be used when knowledge of the marginal distributions is lacking.

中文翻译:

双变量零膨胀计数数据的 Kendall tau 估计器

在本文中,我们扩展了 Pimentel 等人的工作。(2015) 并提出了一个调整后的 Kendall 的 $\tau$ 估计量,用于二元零膨胀计数数据。我们提供了我们提出的估计器可实现的下限和上限,并展示了它与当前文献的关系。此外,我们还建议对可实现的界限进行估计,从而帮助从业者在使用真实数据时解释结果。与 Pimentel 等人的未调整估计器相比,Kendall 的 $\tau$ 的建议估计器的性能是无偏的,均方误差更小。(2015 年)。我们的结果还表明,当缺乏边际分布的知识时,可以使用有界估计器。
更新日期:2022-08-08
down
wechat
bug