专家简介：郭旭，博士，现为北京师范大学统计学院副教授，博士生导师。他于2014年获得博士学位。郭旭一直从事回归分析中的复杂统计推断包括模型设定检验和大规模显著性检验等方面的理论和应用研究，在包括统计学顶级期刊JRSSB, JASA和Biometrika等SCI和SSCI期刊发表论文30篇左右，为包括Econometrica，JASA，Journal of Econometrics，Statistica Sinica等统计学和计量经济学期刊审稿。先后主持国家自然科学基金青年基金和国家自然科学基金面上项目。曾荣获北师大第十一届“最受本科生欢迎的十佳教师”。
报告摘要：This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. With the aid of new reformulation, we propose a new test statistic and show that its asymptotic distribution is $\chi^2$ distribution whose degree of freedom does not depend on the unknown population distribution. We further conduct power analysis under local alternative hypotheses. In addition, we study how to control the false discovery rate of the proposed $\chi^2$ tests, which are correlated, to identify important predictors under a model-free framework. To this end, we propose a multiple testing procedure and establish its theoretical guarantees. Monte Carlo simulation studies are conducted to assess the performance of the proposed tests and an empirical analysis of a real-world data set is used to illustrate the proposed methodology.
腾讯会议号：408 205 747
时间：2022年3月9日 10:00