学术报告
Improve Wilcoxon-Mann-Whitney Test for Multivariate Data through Pairwise Distances——许王莉 教授（中国人民大学）
“2020首师大青年统计论坛”系列报告
题目：Improve Wilcoxon-Mann-Whitney Test for Multivariate Data through Pairwise Distances
报告人：许王莉 教授（中国人民大学）
时间：2020年12月30周三下午19:00-20:00
地点：线上腾讯会议（会议号：766 943 031）
Abstract : The Wilcoxon-Mann-Whitney test is designed to test for homogeneity of two random samples in the univariate case. It is very powerful to detect location shifts yet may lose power completely when there exists scale differences. We improve the classic Wilcoxon-Mann-Whitney test through using pairwise distances of all observations. The improved test can be readily used even when the random observations are multivariate. It is also very powerful in the presence of scale differences. The improved test is in spirit to compare difference between the distribution functions of two random samples. It is {mn/(m + n)}-consistent under the strong null and local alternatives, and root-{mn/(m + n)}-consistent under fixed alternatives, where m,n stand for the respective sizes of the two random samples. The power of the improved test is asymptotically independent of m/n, the size ratio of the two random samples. This indicates that the improved test has nontrivial power as long as the sample sizes are not extremely unbalanced. We demonstrate the theoretical properties of our improved rank-based two-sample test through comprehensive numerical studies.
报告人简介：许王莉，中国人民大学统计学教授，医学与生物统计教研室主任，统计学院学术委员会副主任，中国人民大学教学督导专家。2010 年先后入选“新世纪优秀人才计划”和“北京市科技新星计划”。 近年来一直从事模型拟合优度检验，高维数据分析，随机缺失数据，两阶段抽样数据以及纵向数据分析等方面的统计推断研究。先后主持了4项国家自然科学基金，教育部人文社会科学重点研究基地重大项目，北京市自然科学基金重点项目和教育部人文社科基金等多项科研课题, 在统计学国际一流期刊（包括顶尖期刊）发表论文70余篇，并在科学出版社合作出版《非参数蒙特卡洛检验及其应用》和单著《缺失数据的模型检验及其应用》。
联系人：周洁、胡涛
举办单位：77779193永利官网统计系 、北京应用统计学会、
交叉科学研究院
