报告时间：2021年7月28日 上午10:00-11:00
报告地点：腾讯会议（会议ID：557 485 253）
报告嘉宾：张金廷
报告主题：A Further Study on Chen–Qin’s Test for Two-Sample Behrens-Fisher Problems for High-Dimensional Data
报告摘要
A further study on Chen–Qin’s test, namely CQ-test, for two-sample Behrens-Fisher problems for high-dimensional data is conducted, resulting in a so-called normal-reference test where the null distribution of the CQ-test statistic is approximated with that of a chi-square-type mixture which is obtained from the CQ-test statistic when the null hypothesis holds and when the two samples are normally distributed. The distribution of the chi-square-type mixture can be well approximated by a three-cumulant matched χ2-approximation with the approximation parameters consistently estimated from the data. The asymptotical power of the proposed normal-reference test under a local alternative is established. Two simulation studies demonstrate that in terms of size control, the proposed normal- reference test with the three-cumulant matched χ2-approximation performs well regardless if the data are nearly uncorrelated, moderately correlated, or highly correlated and it performs much better than the CQ-test. A real data example illustrates the proposed normal-reference test.
个人简介
张金廷教授是新加坡国立大学概率统计系终身教授，博士生、博士后导师。早年于北京大学取得学士学位，中国科学院应用数学所取得硕士学位，美国北卡罗来纳大学教堂山分校获得博士学位，美国哈佛大学博士后。先后在美国普林斯顿、罗彻斯特等大学做高级访问学者。张教授现在的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。张教授现任和曾任多家学术期刊的编委，现任美国数理统计学会（IMS）会士，泛华统计协会（ICSA）会士。
