郭旭，博士，现为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及应用研究，近年来旨在对高维数据发展适当有效的检验方法。部分成果发表在JRSSB, JASA，Biometrika和JOE。曾荣获北师大第十一届“最受本科生欢迎的十佳教师”，北师大第十八届青教赛一等奖和北京市第十三届青教赛三等奖。

  报告摘要：Inference of instrumental variable regression models with many weak instruments attracts many attentions recently. To extend the classical Anderson-Rubin test to high-dimensional setting, many procedures adopt ridge-regularization. However, we show that it is not necessary to consider ridge-regularization. Actually we propose a new quadratic-type test statistic which does not involve tuning parameters.

  Our quadratic-type test exhibits high power against dense alternatives. While for sparse alternatives, we derive the asymptotic distribution of an existing maximum-type test, enabling the use of less conservative critical values. To achieve strong performance across a wide range of scenarios, we further introduce a combined test procedure that integrates the strengths of both approaches. This combined procedure is powerful without requiring prior knowledge of the underlying sparsityof the first-stage model. Compared to existing methods, our proposed tests are easy to implement, free of tuning parameters, and robust to arbitrarily weak instruments as well as heteroskedastic errors. Simulation studies and empirical applications demonstrate the advantages of our methods over existing approaches.