报告题目：Renewable Quantile Regression with Heterogeneous Streaming Datasets
时 间：2022年10月19日（星期三）下午2:00pm
地 点：腾讯会议（会议号：316 176 507）
主 办：数学与统计学院、福建省分析数学及应用重点实验室、福建省应用数学中心（福建师范大学）
参加对象：感兴趣的老师和研究生
报告摘要：The renewable statistical inference have 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.
报告人简介：陈雪蓉，西南财经大学统计学院/统计研究中心副教授、博士生导师，西南财经大学光华英才工程入选者，四川省学术技术带头人后备人选。云南大学和中科院数学与系统科学研究院联合培养博士，美国密苏里大学统计系、乔治城大学生物统计博士后，香港城市大学、香港大学和密歇根大学生物统计系访问学者。其研究方向包含：大数据分析，隐私保护，不完全数据分析及非参数半参数建模推断，生存分析，复杂数据建模推断。在JASA, Biometrics, Journal of Business & Economic Statistic等统计学、计量经济学知名杂志上发表论文二十余篇，其中其缺失数据分位数回归的工作获得了教育部“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”。主持国家自然科学基金青年项目一项、面上项目一项，重点项目子课题一项。中国优选法统筹法与经济数学研究会数据科学分会常务理事，中国现场统计研究会经济与金融统计分会常务理事中国现场统计研究会资源与环境统计分会副秘书长及常务理事。