Bergsma-Dassios符号协方差精确方差的新算法

A New Algorithm for the Exact Null Variance of the Sign Covariance of Bergsma-Dassios

  • 摘要: 为度量两个随机变量间的独立性,最近文献基于著名的Kendall’s τ相关系数提出了一种新的度量指标τ*,即Bergsma-Dassios符号协方差.本文利用自平衡二叉查找树中的红黑树算法,计算出了τ*的样本U型估计量t*n=4,5,6,7时的精确分布列,并利用t*n=4,5,6,7时的精确分布列,结合U-统计量相关理论,找到了一种无需求解t*的核函数的各阶投影的具体代数表示形式便可计算出它的核函数的各阶投影的精确方差的方法,由此给出了任意样本容量n≥4的情况下t*的精确方差.同时,我们利用t*及其精确方差,进一步探讨了检验两个随机变量间独立性的假设检验问题.最后,我们模拟验证了t*n=4,5,6,7时精确分布的正确性以及假设检验的有效性.

     

    Abstract: To measure the independence of two random variables,τ* is proposed based on well-known Kendall’s τ correlation coeffcient,which is Bergsma-Dassios sign covariance.In this paper,a method for calculating the exact distribution of t*,the empirical version of τ*,is given by using the red-black tree algorithm in the self-balancing binary search tree.Furthermore,by using the exact distribution of t* when n=4,5,6,7,the exact variance of the projection of the kernel function of t* can be calculated without solving the algebraic representation of the projection of the kernel function of t*.Meanwhile,we utilized t* and its exact variance to further investigate the hypothesis testing problem of examining the independence between two random variables.Finally,the simulation result verifies the accuracy of the exact distribution of t* when n=4,5,6,7 and the validity of the hypothesis test.

     

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