点击次数：

论文类型：期刊论文

发表时间：2021-01-10

发表刊物：COMPUTATIONAL STATISTICS

卷号：35

期号：4

页面范围：1685-1713

ISSN号：0943-4062

关键字：Spearman's rho; Kendall's tau; Dependence; U-statistic; Copula

摘要：Inspired by the correlation matrix and based on the generalized Spearman's rho and Kendall's tau between random variables proposed in Lu et al. (J Nonparametr Stat 30(4):860-883, 2018), rho-matrix and tau-matrix are suggested for multivariate data sets. The matrices are used to construct the rho-measure and the tau-measure among random vectors with statistical estimation and the asymptotic distributions under the null hypothesis of independence that produce the nonparametric tests of independence for multiple vectors. Simulation results demonstrate that the proposed tests are powerful under different grouping of the investigated random vector. An empirical application to detecting dependence of the closing price of a portfolio of stocks in NASDAQ also illustrates the applicability and effectiveness of our provided tests. Meanwhile, the corresponding measures are applied to characterize strength of interdependence of that portfolio of stocks during the recent two years.