报告题目：A fast and accurate kernel-based independence test
时 间：2024年1月16日（星期二）10：00
地 点： 理工北楼601
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：Testing the dependency between two random variables is a vital statistical inference problem since many statistical procedures rely on the assumption that the two samples are independent. A so-called HSIC (Hilbert-Schmidt Independence Criterion)-based test has been proposed to test whether two samples are independent. Its null distribution is approximated either by permutation or a Gamma approximation. Unfortunately, the permutation-based test is very time-consuming, and the Gamma-approximation-based test does not work well for high-dimensional data. A new HSIC-based test is proposed. Its asymptotic null and alternative distributions are established. It is shown that the proposed test is root-n consistent. A three-cumulant matched chi-squared approximation is adopted to approximate the null distribution of the test statistic. The proposed test can be applied to many different data types, including multivariate, high-dimensional, and functional data, by choosing a proper reproducing kernel. Three simulation studies and two real data applications show that the proposed test outperforms several tests for multivariate, high-dimensional, and functional data in terms of level accuracy, power, and computational cost.
报告人简介：张金廷教授是新加坡国立大学统计与数据科学系终身教授，博士生、博士后导师。1988年在北京大学取得学士学位，1991年在中国科学院应用数学所取得硕士学位，1999年在美国北卡罗来纳大学教堂山分校获得博士学位，2000年在美国哈佛大学做博士后。张金廷教授先后在美国普林斯顿、罗彻斯特等大学做高级访问学者。主要研究领域包括非参数统计，纵向数据分析，函数数据分析，高维数据分析等，在Annals of Statistics（世界统计年刊），JASA（美国统计学会杂志），JRSSB（英国皇家统计学会杂志），Statistics Sinica（统计学报）等统计学国际顶级期刊上发表文章50余篇；著有两本英文统计学专著《Analysis of Variance for Functional Data》和《Nonparametric Regression Methods for Longitudinal Data Analysis》，以及一本学术论文集。现任和曾任多家重要学术期刊的副主编或者编委，并先后六次担任大型国际会议的组织委员。