一、报告时间

2025年10月13日（星期一）15:00

二、报告形式

腾讯会议：393957767

三、报告人

王江峰浙江工商大学教授

四、报告主题

Linear extremile regression and its semi-supervised learning

五、报告摘要

The extremile (Daouia et al., 2019) is a novel and coherent measure of risk, determined by weighted expectations rather than tail probabilities. However, existing studies on extremile are difficult to generalize to linear models due to the challenge of obtaining a

-consistent estimator of unknown parameters in the model. To address this issue, this article proposes a new definition of linear extremile regression and its estimation method. In many practical data analyses, linear regression models may not be accurate. Therefore, we have developed a semi-supervised framework for the proposed linear extremile regression using unlabeled data, which is often present in practical problems. Both simulations and real data analyses have been conducted to illustrate the finite sample performance of the proposed methods.

六、报告人简介

王江峰，博士(后)，浙江工商大学统计学教授，博士生导师，西湖学者拔尖人才，全国工业统计学教学研究会理事。目前研究方向：复杂生存数据分析、分位数回归方法、高维数据分析、经验似然方法、时空数据分析等。主持国家社科基金一般项目3项、教育部人文社科基金等省部级项目5项；在《中国科学》、《数学学报》(中、英文版)、《JRSSB》、《IEEE Trans. Cybern》、《Front. Math. China》、《Statistics》、《J. Statist. Plann. Inference》等发表论文40余篇。