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题目：Renewable Quantile Regression with Heterogeneous Streaming Datasets

摘要：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.

简介：陈雪蓉，西南财经大学“光华杰出学者计划”青年杰出教授、博士生导师，国家级青年人才计划入选者，四川省高层次人才入选者。中科院数学与系统科学研究院博士（联合培养），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。论文发表于JASA, JCGS, Biometrics, JBES等统计学、计量经济学权威期刊。主持国家自然科学基金面上项目2项、青年项目、国家自然科学基金重点项目子课题、国家重点研发计划课题子课题各1项。曾荣获教育部“第八届高等学校科学研究优秀成果奖青年成果奖”。

时间 3月12日 18：30-19：30.

腾讯会议号 ：200615055

联系人：胡涛