Use of random integration to test equality of high dimensional covariance matrices
报告人：Prof. Xueqin Wang（School of Management, University of Science and Technology of China）
时间：2020-05-29 15:00-17:00
地点：会议ID: 121-657-923; 腾讯会议链接：https://meeting.tencent.com/s/LhGNQ3hiE7Si
Abstract: Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. A common approach is to obtain consistent estimates of the covariance matrices before constructing a test statistic. However, estimating the covariance matrices for high-dimensional is also difficult, albeit sometimes alleviated by assumptions such as sparsity. Through a novel use of random integration, we can test the equality of high-dimensional covariance matrices without estimating them and without assuming parametric distributions for the two underlying populations, even if the dimension is much larger than the sample size. The asymptotic properties of our test for arbitrary number of covariates and sample size are studied in depth under a high-dimensional factor model. The finite-sample performance of our test is evaluated through numerical studies. The empirical results demonstrate that our test is highly competitive with existing tests in a wide range of settings. In particular, our proposed test is distinctly powerful under difficult settings when there are only a few large or many small diagonal disturbances between the two covariance matrices.
Bio: 王学钦，中国科学技术大学管理公司教授。2003年毕业于纽约州立大学宾厄姆顿分校, 2012年入选教育部新世纪优秀人才支持计划学者， 2013年获得国家优秀青年研究基金，2014年入选第八批广东省高等学校“千百十工程”国家级培养计划，2016年入选“广东特支计划”（百千工程领军人才）。此外，他还担任教育部高等学校统计学类专业教学指导委员会委员、统计学国际期刊《JASA》、《SII》、《JCS》的Associate Editor、高等教育出版社《Lecture Notes: Data Science, Statistics and Probability》系列丛书的副主编、中国现场统计研究会数据科学与人工智能分会副理事长和中国青年统计学家协会副会长等。
Place: 腾讯会议
会议 ID：121-657-923
会议密码：123123
会议链接：https://meeting.tencent.com/s/LhGNQ3hiE7Si
Your participation is warmly welcomed!
