郭旭，现任北京师范大学统计学院教授，博士生导师。现主持国自然B类项目。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。目前主要关注高维回归模型中的假设检验问题也对机器学习算法在统计推断中的作用感兴趣，已发表高水平学术论文40余篇，包括统计学和计量经济学国际顶尖期刊JRSSB，JASA, Biometrika, JOE, JBES和机器学习顶会NeurIPS。

  报告摘要：In this paper, we investigate the adequacy testing problem of high-dimensional factor-augmented regression model. Existing test procedures do not perform well under dense alternatives. To address this critical issue, we introduce a novel quadratic-type test statistic which can efficiently detect dense alternative hypotheses. We further propose an adaptive test procedure to remain powerful under both sparse and dense alternative hypotheses.

Theoretically, under the null hypothesis, we establish the asymptotic normality of the proposed quadratic-type test statistic and asymptotic independence of the newly introduced quadratic-type test statistic and a maximum-type test statistic. We also prove that our adaptive test procedure is powerful to detect signals under either sparse or dense alternative hypotheses. Simulation studies and an application to the Federal Reserve Economic Data-Monthly Database are carried out to illustrate the merits of our introduced procedures.