摘要：

We study estimation and inference for high-dimensional linear models with two types of “proxy data”. The first type of proxies encompasses marginal statistics and sample covariance matrices computed from distinct sets of individuals. We develop a rate optimal method for estimation and inference for the regression coefficient vector and its linear functionals based on the proxy data. We show the intrinsic limitations in the proxy-data based inference: the minimax optimal rate for estimation is slower than that in the conventional case where individual data are observed. The second type of proxy data is differentially private data. We propose method for private estimation and inference in high-dimensional regression with FDR control.

报告人简介：

中国人民大学统计与大数据研究院准聘副教授、博士生导师，2018年博士毕业于美国罗格斯大学统计系，之后在美国宾夕法尼亚大学医学院生物统计系和沃顿商学院做博士后，2021年加入中国人民大学统计与大数据研究院。研究方向包括高维统计推断、机器学习和遗传学驱动的统计方法和理论，基于工具变量的因果推断等。