时 间：2025-04-09 （周三）16:00 -17:00
报告人：张宇谦 中国人民大学助理教授
地 点：普陀校区理科大楼A1514
主持人：马慧娟 华东师范大学副教授
摘 要：
In modern data analysis, information is frequently collected from multiple sources, often leading to challenges such as data heterogeneity and imbalanced sample sizes across datasets. Robust and efficient data integration methods are crucial for improving the generalization and transportability of statistical findings. In this work, we address scenarios where, in addition to having full access to individualized data from a primary source, supplementary covariate information from external sources is also available. While traditional data integration methods typically require individualized covariates from external sources, such requirements can be impractical due to limitations related to accessibility, privacy, storage, and cost. Instead, we propose novel data integration techniques that rely solely on external summary statistics, such as sample means and covariances, to construct robust estimators for the mean outcome under both homogeneous and heterogeneous data settings. Additionally, we extend this framework to causal inference, enabling the estimation of average treatment effects for both generalizability and transportability.
报告人简介：
张宇谦，中国人民大学统计与大数据研究院助理教授，博士生导师。2016年本科毕业于武汉大学，2022年博士毕业于美国加州大学圣地亚哥分校。主要研究方向包括因果推断、半监督学习、高维统计、机器学习理论、缺失数据、精准医疗等。文章发表于Annals of Statistics、Biometrika、Information and Inference等期刊。主持国家自然科学基金青年项目一项，参与面上项目一项。曾获美国统计协会非参数统计组最佳学生论文奖。