报告题目：A data-driven approach for independence testing in multivariate time series based on Chatterjee's rank correlation
时 间：2025年 4月30 日（星期三）10:00
地 点：科研楼18号楼1102
主 办：数学与统计学院
参加对象：感兴趣的老师和学生
报告摘要：Verifying whether a multivariate time series follows an independent and identically distributed (i.i.d.) sequence is a critical issue.This paper introduces a novel method for testing this assumption by analyzing the dependency structure inherent in the data. Specifically, we utilize Chatterjee's rank correlation to develop a new analytical tool, termed the auto-Chatterjee's rank correlation matrix (ACRCM).Each element of the ACRCM quantifies Chatterjee's rank correlation, effectively capturing nonlinear dependencies within the observed series.We develop an ACRCM-based statistic with fixed order for testing independence in multivariate time series, which asymptotically follows a chi-squared distribution under the assumption of independence. Furthermore, we introduction a data-driven approach for automatically determining the optimal order based on the data characteristics.This data-driven approach offers three key advantages:first, it eliminates the need for manually specifying the order, as the optimal order is automatically selected based on the data;second, under the null hypothesis, the selected order is one, and the chi-square distribution has degrees of freedom correspond to the square of the data dimension;third, the proposed data-driven approach demonstrates superior sensitivity to detecting high-order dependencies.We rigorously derive the asymptotic properties of the proposed method and validate its effectiveness through extensive simulation experiments.
报告人简介：王国长，暨南大学经济学院统计与数据科学系，教授、博士生导师，国家级青年人才计划入选者。主要研究方向为函数型数据，时间序列和机器学习，至今在JoE、JBES、Sinica和Scandinavian Journal of Statistics等重要学术期刊发表论文30余篇。主持国家级项目4项，省部级项目4项。任中国现场统计研究会资源与环境统计分会常务理事；中国旅游大数据协会，理事，副秘书长；广东省现场统计协会常务理事，秘书长。