Model-Free Statistical Inference on High- Dimensional Data

报告人：郭旭

报告地点：腾讯会议ID: 436411873

报告时间：2020年11月29日星期五09:00-10:00

报告摘要：

This talk will present 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. We further conduct power analysis under local alternative hypotheses. In addition, we study how to control the false discovery rate of the proposed χ2 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 dataset is used to illustrate the proposed methodology. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

主讲人简介：

郭旭，现任北京师范大学统计学院教授，博士生导师。一直从事回归分析中复杂假设检验的理论方法和应用研究，目前主要关注高维数据中的假设检验问题。现主持国家自然科学基金优秀青年基金。曾荣获北京师范大学第十一届“最受本科生欢迎的十佳教师”，北京师范大学第十八届“青教赛”一等奖和北京市第十三届“青教赛”三等奖。现担任《Journal of Systems Science and Complexity 》的青年编委和《应用概率统计》期刊第十届编委。目前，已发表（含接受）学术论文30余篇，包括统计学国际顶尖期刊Journal of the Royal Statistical Society: Series B (JRSSB)，Journal of the American Statistical Association (JASA)和Biometrika（共7篇），计量经济学顶级期刊Journal of Econometrics (JOE)1篇。