题目：Residual Importance Weighted Transfer Learning For High-dimensional Linear Regression

报告人：赵俊龙教授 北京师范大学

时间：2025年4月28日15:00-16:00

腾讯会议：273-999-304

摘要：Transfer learning is an emerging paradigm for leveraging multiple source data to improve the statistical inference on a single target data. In this paper, we propose a novel approach named residual importance weighted transfer learning (RIW-TL) for high-dimensional linear models built on LASSO. Compared to existing methods such as Trans-Lasso that selects source data in an all-in-all-out manner, RIW-TL includes samples via importance weighting. To determine the weights, remarkably RIW-TL only requires one-dimensional density estimation by weighting residuals, thus overcoming the curse of dimensionality of having to estimate high-dimensional densities in naive importance weighting. We show that the oracle RIW-TL provides faster rate than its competitors and develop a crosstting procedure to estimate this oracle. We discuss variants of RIW-TL by adopting dierent choices for residual weighting. The theoretical properties of RIW-TL and its variants are established and compared with those of LASSO and Trans-Lasso. Extensive simulations and a real data analysis conrm the advantages of RIW-TL.

简介：赵俊龙，北京师范大学统计学院教授。主要从事统计学和机器学习相关研究，包括：高维数据分析、稳健统计，统计机器学习等。在统计学各类期刊发表论文五十余篇，部分结果发表在统计学国际顶级期刊JRSSB，AOS、JASA，Biometrika，JBES等。主持多项国家自然科学基金项目，参与国家自然科学基金重点项目。任中国现场统计学会高维数据分会、北京大数据学会等多个学术分会理事或常务理事。