Central limit theorem for high-dimensional R2 statistics under a general independent components model

报告人：李卫明

报告地点：数统楼403教室

报告时间：2022年01月05日星期三14:00-15:00

报告摘要：

This paper establishes a central limit theorem (CLT) for R2 statistics in a moderately high- dimensional asymptotic framework. The underlying population accommodates a general independent components model, by virtue of which our result unifies the two CLTs proposed separately in Zheng et al. (2014) and Guo and Cheng (2021). Beyond this, the new CLT demonstrates and quantifies a non-negligible impact of kurtosis parameters of the latent independent components on the fluctuation of R2 statistics. As an application, a novel confidence interval is constructed for the coefficient of multiple correlation in a high-dimensional linear regression.

主讲人简介：

李卫明，2010年毕业于东北师范大学，获博士学位。2016/07-至今，任教于上海财经大学，统计与管理学院，副教授。研究领域包括高维统计分析，随机矩阵理论等。