报告时间：2026年6月6日（周六）下午 15:00-17:00
报告地点：天博体育 天赐庄校区博远楼304
报告人：杜悦 博士生，西南财经大学
报告摘要：
We propose new statistical tests, in high-dimensional settings, for testing the independence of two random vectors and their conditional independence given a third random vector. The key idea is simple, i.e., we first transform each component variable to the standard normal via its marginal empirical distribution, and we then test for independence and conditional independence of the transformed random vectors using appropriate L∞-type test statistics. While we are testing some necessary conditions of the independence or the conditional independence, the new tests outperform the 13 frequently used testing methods in a large scale simulation comparison. The advantage of the new tests can be summarized as follows: (i) they do not require any moment conditions, (ii) they allow arbitrary dependence structures of the components among the random vectors, and (iii) they allow the dimensions of random vectors to diverge at the exponential rates of the sample size. The critical values of the proposed tests are determined by a computationally efficient multiplier bootstrap procedure. Theoretical analysis shows that the sizes of the proposed tests can be well controlled by the nominal significance level, and the proposed tests are also consistent under certain local alternatives. The finite sample performance of the new tests is illustrated via extensive simulation studies and a real data application.
报告人简介：
杜悦，西南财经大学数据科学与商业智能联合实验室博士生。主要从事超高维数据假设检验和矩阵时间序列分析等领域的研究。在统计学期刊The Annals of Statistics和Journal of the American Statistical Association发表论文。
邀请人：张园园