报告题目：Testing relevant difference in high-dimensional linear regression models and its application to detecting transferability
报告人： 刘旭教授 上海财经大学
报告时间：2024年12月26日星期四下午16：00
报告地点：至善楼234
邀请人： 邱世芳教授
报告摘要：
Most of researchers on testing a significance of coefficients in high-dimensional regression models consider the classical hypothesis testing problem. We take a different perspective and study the testing problem with the null hypothesis of no relevant difference between coefficient and zero. This testing problem is motivated by the urgent requirement to detect the transferability of source data in the transfer learning framework. We propose a novel test procedure incorporating the estimation of the largest eigenvalue of a high-dimensional covariance matrix with the assistance of the random matrix theory. In the more challenging setting in the presence of high-dimensional nuisance parameters, we establish the asymptotic normality for the proposed test statistics under both the null and alternative hypotheses. By applying the proposed test approaches to detect the transferability of source data, the unified transfer learning models simultaneously achieve lower estimation and prediction errors with comparison to existing methods. We study the finite-sample properties of the new test by means of simulation studies and illustrate its performance by analyzing the GTEx data.
报告人简介：
刘旭，上海财经大学统计与管理学院常任轨教授。2011年博士毕业于云南大学。2011-2016年分别在美国西北大学和密歇根州立大学从事博士后研究。近年来主要研究兴趣为生成式学习、迁移学习、以及高维数据分析。在国际权威统计期刊包括JASA，Biometrika，JoE，JMLR等发表近30篇论文。现担任International Journal of Organizational and Collective Intelligence (IJOCI) 和 Journal of Statistical Theory and Applications (JSTA)的副主编。主持两项国家自科面上项目、负责一项国家自科重点项目子课题。
承办单位：理学院、数学科学研究中心
