论文标题
在限制排列图上使用随机步行图测试双变量审查数据的独立性
Testing Independence of Bivariate Censored Data using Random Walk on Restricted Permutation Graph
论文作者
论文摘要
在本文中，我们提出了一个测试双变量审查数据独立性的程序，该数据是通用的，适用于文献中的任何审查类型。为了检验假设，我们考虑了基于等级的统计量，即肯德尔的TAU统计数据。审查的数据定义了观测值所有可能等级的限制置换空间。我们提出了统计数字，肯德尔（Kendall）的tau在受限置换空间中的平均值。为了评估统计量及其参考分布，我们开发了马尔可夫链蒙特卡洛（MCMC）程序，以在受限的置换空间上获得统一的样品，并在数值上近似于平均的肯德尔的tau的零分布。我们将过程应用于具有不同审查类型的三个真实数据示例，并将结果与现有方法进行比较。我们以本文主体的主体没有进行的一些其他讨论来结束论文。
In this paper, we propose a procedure to test the independence of bivariate censored data, which is generic and applicable to any censoring types in the literature. To test the hypothesis, we consider a rank-based statistic, Kendall's tau statistic. The censored data defines a restricted permutation space of all possible ranks of the observations. We propose the statistic, the average of Kendall's tau over the ranks in the restricted permutation space. To evaluate the statistic and its reference distribution, we develop a Markov chain Monte Carlo (MCMC) procedure to obtain uniform samples on the restricted permutation space and numerically approximate the null distribution of the averaged Kendall's tau. We apply the procedure to three real data examples with different censoring types, and compare the results with those by existing methods. We conclude the paper with some additional discussions not given in the main body of the paper.
