报告题目：Two-sample tests for equal distributions in separable metric spaces
时 间：2024年12月30日（星期一）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：With the advancement of data collection techniques, researchers frequently encounter complex data objects within separable metric spaces across various domains. One common interest lies in determining whether two groups of complex data objects originate from the same population. This paper introduces and examines a fast and accurate unified semimetric-based approach designed to tackle this challenge. The approach exhibits broad applicability across a wide range of research areas, such as bioinformatics, audiology, environmentology, finance, and more. It effectively identifies differences between the distributions of two complex datasets, including both high-dimensional data and functional data. The asymptotic null and alternative distributions of the proposed test statistic are established. Unlike the permutation approach, a unified, rapid and precise method to approximate the null distribution is described. Furthermore, the proposed test is shown to be root-n consistent. Numerical results are presented for illustrating the excellent performance of the proposed test in terms of size control, power, and computational cost. Additionally, the applications of the proposed test are showcased through examples involving both high-dimensional data and functional data.
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。