报告题目：Statistical inference for smoothed quantile regression with streaming data
报告人：谢锦瀚博士后研究员(加拿大阿尔伯塔大学)
主持人：李娜教授(yh0612cc银河副院长)
报告地点：燕山校区1号教学楼1502
报告时间：2023年11月20日（星期一）11：00-12：00
主办单位：yh0612cc银河
摘要：
In this paper, we address the problem of how to conduct valid statistical inference for quantile regression with streaming data. The main difficulties are that the quantile regression loss function is non-smooth and it is often infeasible to store the entire dataset in memory, which invalidates the use of the existing methodology. We propose a fully online updating method for statistical inference in smoothed quantile regression with streaming data to overcome these issues. Our main contributions are twofold. First, in the low-dimensional regime, we present an incremental updating algorithm to obtain the smoothed quantile regression estimator with the streaming data set. The proposed estimator allows us to construct asymptotically exact statistical inference procedures. Second, in the high- dimension regime, we develop an online debiased lasso procedure to accommodate the special sparse structure of streaming data. The proposed online debiased approach is updated with only the current data and summary statistics of historical data and corrects an approximation error term from online updating with streaming data. Moreover, theoretical results such as estimation consistency and asymptotic normality are established to justify its validity in both settings. Simulation studies with supportive evidence are presented. Applications are illustrated with the Seoul bike sharing demand data and the index fund data.
报告人简介：
谢锦瀚，于2019年在云南大学获得理学博士学位，2019年至2020年在香港中文大学统计系从事博士后研究工作，现今为加拿大阿尔伯塔大学(University of Alberta)博士后研究员。在统计学顶级期刊JASA，JBES以及人工智能顶级会议AAAI等发表论文10余篇主要研究领域包括高维数据分析，在线学习，数据隐私保护等。