Testing independence for multivariate time series via the auto-distance correlation matrix
提出了多元时间序列的矩阵自距离协方差和相关函数，并开发了检验独立同分布假设的方法，性能优于多元Ljung-Box检验。
We introduce the matrix multivariate auto-distance covariance and correlation functions for time series, discuss their interpretation and develop consistent estimators for practical implementation. We also develop a test of the independent and identically distributed hypothesis for multivariate time series data and show that it performs better than the multivariate Ljung–Box test. We discuss computational aspects and present a data example to illustrate the method.