【专家简介】：唐炎林，华东师范大学统计学院教授，博士生导师，统计学系主任；入选国家“万人计划”青年拔尖人才、上海市浦江人才计划。2012年1月博士毕业于复旦大学统计系，同年5月加入同济大学，2019年1月加入华东师范大学。主要研究方向为分位数回归、高维统计推断、不完全数据统计建模，主持多项国家自然科学基金、上海市自然科学基金，担任SCI期刊Statistica Sinica、Journal of the Korean Statistical Society的编委。在Biometrika、JRSSB、PNAS、Biometrics等发表论文40余篇。
【报告摘要】：Conformal prediction is a distribution-free method for uncertainty quantification that ensures finite sample guarantee. However, its validity relies on the assumption of data exchangeability. In this talk, I will introduce several conformal prediction approaches tailored for non-exchangeable data settings, including clustered data with missing responses, nonignorable missing data, and label shift data. To provide an asymptotic conditional coverage guarantee for a given subject, we propose constructing prediction regions by establishing the highest posterior density region of the target. This method is more accurate under complex error distributions, such as asymmetric and multimodal distributions, making it beneficial for personalized and heterogeneous scenarios. I will present some numerical results to illustrate their effectiveness.
【报告时间】：2025年03月06日 16:00-17:00
【报告地点】：位育楼417