报告题目：TRANSFER LEARNING FOR HIGH-DIMENSIONAL QUANTILE REGRESSION VIA CONVOLUTION SMOOTHING

报告简介：This paper studies the high-dimensional quantile regression problem under the transfer learning framework, where possibly related source datasets are available to make improvements on the estimation or prediction based solely on the target data. In the oracle case with known transferable sources, a smoothed two-step transfer learning algorithm based on convolution smoothing is proposed and the ℓ1/ℓ2 estimation error bounds of the corresponding estimator are also established. To avoid including non-informative sources, we propose to select the transferable sources adaptively and establish its selection consistency under regular conditions. Monte Carlo simulations as well as an empirical analysis of gene expression data demonstrate the effectiveness of the proposed procedure.

Key words and phrases: High-dimensional data; Quantile regression; Regularization; Smoothing; Transfer learning.

报告人：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五项、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面上、重点和天元各一项。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)。获得教育部自然科学二等奖一次。

报告时间：2023年11月10日（星期五）下午 2:30-3:30.

报告地点：瘦西湖校区56号楼208

                 腾讯会议，ID： 693-287-4087

主办单位：扬州大学数学科学学院

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