报告题目：Distribution-Free Prediction Sets for Regression under Target Shift
时 间：2026年5月11日（星期一）16：00
地 点：科研楼18号楼1102
主 办：数学与统计学院、分析数学及应用教育部重点实验室、统计学与人工智能福建省高校重点实验室
参加对象：感兴趣的老师和学生
报告摘要：In real-world applications, the limited availability of labeled outcomes presents significant challenges for statistical inference, due to high collection costs, technical barriers, and other constraints. In this work, we construct efficient conformal prediction sets for new target outcomes by leveraging a source distribution—distinct from the target but related through a distributional shift assumption—that provides abundant labeled data.When the target data are fully unlabeled, our predictions rely solely on the source distribution; when partial labels are available, they are integrated with the source data to improve efficiency. To address the challenges of data non-exchangeability and distribution non-identifiability, we identify the likelihood ratio by matching the covariate distributions of the source and target domains within a finite B-spline space. To accommodate complex error structures such as asymmetry and multimodality, our method constructs the highest predictive density sets using a novel weight-adjusted conditional density estimator. This estimator models the source conditional density along a quantile process and transforms it—via appropriate weighting adjustments—to approximate the target conditional density. We establish the theoretical properties of the proposed method and evaluate its finite-sample performance through simulation studies and a real-data application to the MIMIC-III clinical database.
报告人简介：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；入选国家青年高层次人才计划、上海市浦江人才计划。主要研究方向为分位数回归、共形预测、高维异质性数据统计推断，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等发表论文近50篇。