報告題目： High-dimensional independence testing
報 告 人： Han Fang 教授 The Department of Statistics, University of Washington
報告時間： 2023年5月19日 10:00
報告地點： Zoom meeting: 822 2451 3801 Password: 230519
校内聯系人：韓月才 hanyc@jlu.edu.cn
報告摘要：In this talk I will revisit two earlier papers of mine on minimax-optimal high-dimensional independence testing using rank correlations. The test statistics are rank-based, of a maximum-type, and valid when both the dimension and sample size increase to infinity. The proof is based on U-statistics theory, Stein's method, Zaitsev's Gaussian approximation theory, and minimax arguments.
報告人簡介：Han Fang is an associate professor in statistics, in economics (adjunct) at the University of Washington, and an affiliated investigator in Fred Hutchinson Cancer Research Center. He obtained his Ph.D. from the Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health in 2015. His research interest includes Rank- and graph-based methods, statistical optimal transport, mixture models, nonparametric and semiparametric regressions, time series analysis, and random matrix theory.
