讲座题目：Transfer Learning for High-dimensional Quantile Regression with Distribution Shift
主讲人：朱仲义 教授
主持人：唐炎林 教授
开始时间：2025-03-17 13:30
讲座地址：普陀校区理科大楼A1514
主办单位：统计学院
报告人简介：
朱仲义，复旦大学统计与数据科学系教授，博士研究生导师；曾任中国概率统计学会第八、九届副理事长,国际著名杂志“Statistica Sinica”副主编; “应用概率统计””中国科学：数学”杂志编委；现为国际数理统计学会当选会员，担任”数理统计与管理”杂志编委和国际顶级统计杂志JASA的副主编。专业研究方向为：纵向数据(面板数据)模型；分位数回归模型，机器学习等。主持完成国家自然科学基金面上项目七项、国家社会科学基金一项，作为子项目负责人完成国家自然科学基金重点项目二项，重大项目子项目一项，目前主持国家自然科学基金重点项目一项。近几年发表论文100多篇(其中包括在国际四大统计和机器学习顶级刊物等SCI论文八十多篇)。2015年获得教育部自然科学二等奖。
报告内容：
Information from related source studies can often enhance the findings of a target study. However, the distribution shift between target and source studies may severely impact the efficiency of knowledge transfer. In the high-dimensional regression setting, existing transfer mainly focus on the parameter shift. In this paper, we focus on the high-dimensional quantile regression with knowledge transfer under three types of distribution shift: parameter shift, covariate shift, and residual shift. We propose a novel transferable set and a new transfer framework to address the above three discrepancies. Nonasymptotic estimation error bounds and source detection consistency are established to validate the availability and superiority of our method in the presence of distribution shift. Additionally, an orthogonal debiased approach is proposed for statistical inference with knowledge transfer, leading to sharper asymptotic results. Extensive simulation results as well as real data applications further demonstrate the effectiveness of our proposed procedure.
