统计与数据科学系系列学术报告之四百一十三期

时   间：2023年11月16日（周四）16:00-17:00

地   点：史带楼503室

主持人：复旦大学 管理学院 统计与数据科学系 张新生 教授

报告人：赵俊龙 教授  北京师范大学统计学院

题   目：Residual Importance Weighted Transfer Learning for High-dimensional Linear Regression

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 allin-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 cross-fitting procedure to estimate this oracle. We discuss variants of RIW-TL by adopting different 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 confirm the advantages of RIW-TL.

个人简介：赵俊龙教授，北京师范大学统计学院博士生导师，应用统计系系主任。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SCI论文近五十篇，部分结果发表在统计学国际顶级期刊JRSSB，AOS、JASA，Biometrika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。 

统计与数据科学系

2023-11-09