时 间：2022年10月13日 10：00
地 点：腾讯会议ID：697-451-529
报告人：王磊 副研究员
主持人：王小舟 助理教授
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 quantile regression model 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 and adopting the smoothing technique 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 an application to a gene expression dataset is also presented.
报告人简介：
王磊，南开大学统计与数据科学学院副研究员，博士生导师。研究方向是复杂数据分析和统计学习，已在Biometrika、Bernoulli、Statistica Sinica、Scandinavian Journal of Statistic、Statistics in Medicine等统计学杂志发表论文50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。