报告人：孔小丽助理教授

             Loyola University Chicago

题目：High Dimensional Multivariate Inference Under General Conditions

时间：2018年07月07日下午16:00

地点：海韵实验楼105

摘要：In this talk, we relax the commonly imposed dependence conditions that seem to be the standard assumption in high-dimension. With the relaxed conditions, the scope of applicability of the results in Chen and Qin (2010) broadens. Then the application to a fully nonparametric rank-based comparison of high-dimensional populations is given.  To develop the theory, we prove a novel result for studying the asymptotic behavior of quadratic forms in ranks. Simulation study shows that the developed rank-based method performs comparably well with mean-based methods. It has significantly superior power for heavy tailed distribution. The application to an EEG data with the objective of examining association between alcohol use and change in brain function is given.

报告人简介：孔小丽于2009年在hg8868官方网站获得数学博士学位，主要研究方向为李代数及其表示论；于2018年在University of Kentucky获得统计学博士学位，主要研究方向为高维数据多变量分析。她现为Loyola University Chicago数学系的Assistant Professor。

联系人：周达副教授

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