Statistical inference for high-dimensional regression with proxy data
报告人：李赛（中国人民大学）
时间：2024-03-21 14:00-15:00
地点：智华楼四元厅-225
Abstract: 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.
About the Speaker:
李赛，中国人民大学统计与大数据研究院准聘副教授、博士员工导师，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公司生物统计系和沃顿商公司做博士后，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。
