报告题目：Renewable Quantile Regression with Heterogeneous Streaming Datasets

报告时间：2023年6月2日 周五 15：00

报告地点：数学楼2-3

Abstract：The renewable statistical inference has received much attention since the advent of streaming data collection techniques. However, most existing online updating methods are developed based on a homogeneity assumption and gradients; all data batches are required to be either independent and identically distributed or share the same regression parameters, and objective functions must be smooth concerning parameters. To our best knowledge, the only existing approach that allows some regression parameters to be different for different data batches, was proposed by Luo and Song (2021) who required the homogeneous structure to be known, which is difficult to guarantee in actual application. In this paper, we develop an online renewable quantile regression method that relies only on the current data and summary statistics of historical data, for both homogeneous and heterogeneous streaming data. The proposed methods are computationally efficient, can automatically detect the unknown potential homogeneous structure,and are robust to heavy-tailed noise and data with outliers. Asymptotic properties show that the proposed renewable estimators can achieve the same statistical efficiency as the oracle estimators based on individual level data. A numerical simulation and a real data analysis illustrate that the proposed methods perform well. Supplementary materials for this article are available online.

报告人简介：陈雪蓉，西南财经大学统计学院/统计研究中心教授、博士生导师、西南财经大学光华英才工程入选者，四川省天府万人计划-天府金融菁英入选者，教育部“第八届高等学校科学研究优秀成果奖（人文社会科学）青年成果奖”获得者。中科院数学与系统科学研究院和云南大学联合培养博士，美国密苏里大学统计系、乔治城大学生物统计博士后，美国密歇根大学、香港城市大学、香港大学访问学者。在JASA,Biometrics,Journal of Business&Economic Statistic等统计学、生物统计学、计量经济学权威期刊上发表论文二十余篇。主持国家自然科学基金面上项目、青年项目、国家自然科学基金重点项目子课题各一项，以项目骨干身份参加国家重点研发计划课题子课题一项。