报告题目：Communication-efficient estimation and inference for large-scale data
报告人：王磊 副研究员 （南开大学统计与数据科学学院）
报告时间：2022年12月20日 （星期二）下午14：00-15：00
腾讯会议 ID：208-629-396
邀请人：王晓光 副教授 联系电话：84708351-8213
报告摘要：Distributed estimation based on different sources of observations has drawn attention in the modern statistical learning. In practice, due to the expensive cost or time-consuming process to collect data in some cases, the sample size on each local site can be small, but the dimension of covariates is large and may be far larger than the sample size on each site. In this paper, we focus on the distributed estimation and inference for a pre-conceived low-dimensional parameter vector in the high-dimensional models with small local sample size. Specifically, we consider that the data are inherently distributed and propose two communication-efficient estimators by generalizing the decorrelated score approach to conquer the slow convergence rate of nuisance parameter estimation based on multi-round algorithms. The risk bounds and limiting distributions of the proposed estimators are given. The finite sample performance of the proposed estimators is studied through simulations and some real data.
报告人简介：王磊，南开大学统计与数据科学学院副研究员，研究方向是复杂数据分析和统计学习，已在Biometrika、SCIENCE CHINA Mathematics、Bernoulli、Statistica Sinica、Scandinavian Journal of Statistic、Statistics in Medicine等统计学杂志发表论文多篇，主持3项国家自然科学基金项目和1项天津市自然科学基金项目。
