当前位置: X-MOL 学术Statistics › 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.)
Modelling count data via copulas
Statistics ( IF 1.9 ) Pub Date : 2020-11-01 , DOI: 10.1080/02331888.2020.1867140
Hadi Safari-Katesari 1 , S. Yaser Samadi 1 , Samira Zaroudi 1
Affiliation  

Copula models have been widely used to model the dependence between continuous random variables, but modelling count data via copulas has recently become popular in the statistics literature. Spearman's rho is an appropriate and effective tool to measure the degree of dependence between two random variables. In this paper, we derive the population version of Spearman's rho via copulas when both random variables are discrete. The closed-form expressions of the Spearman correlation are obtained for some copulas with different marginal distributions. We derive the upper and lower bounds of Spearman's rho for Bernoulli marginals. The proposed Spearman's rho correlations are compared with their corresponding Kendall's tau values and their functional relationships are characterized in some special cases. An extensive simulation study is conducted to demonstrate the validity of our theoretical results. Finally, we propose a bivariate copula regression model to analyse the count data of a cervical cancer dataset.

中文翻译:

通过 copula 对计数数据进行建模

Copula 模型已广泛用于对连续随机变量之间的依赖性进行建模,但通过 copula 对计数数据进行建模最近在统计文献中变得流行。Spearman's rho 是衡量两个随机变量之间依赖程度的合适且有效的工具。在本文中,当两个随机变量都是离散的时,我们通过 copula 推导出 Spearman 的 rho 的总体版本。对于一些具有不同边缘分布的 copula,得到了 Spearman 相关性的闭式表达式。我们推导出伯努利边际的 Spearman 的 rho 的上限和下限。将提出的 Spearman 的 rho 相关性与其相应的 Kendall tau 值进行比较,并在某些特殊情况下表征它们的功能关系。进行了广泛的模拟研究以证明我们的理论结果的有效性。最后,我们提出了一个双变量 copula 回归模型来分析宫颈癌数据集的计数数据。
更新日期:2020-11-01
down
wechat
bug