报告题目：Unconditional Quantile Regression for Streaming Datasets

报告时间： 2024-11-22 15:00—16:00

报告地点： 统计与数据科学学院会议室109

报告人： 姜荣

报告人简介：姜荣，博士，上海对外经贸大学，统计与信息学院，教授，全国工业统计学教学研究会金融科技与大数据技术分会理事。在《Journal of Business & Economic Statistics》、《Journal of Financial Econometrics》、《Test》、《Neurocomputing》和《Journal of Multivariate Analysis》等国际期刊上发表SCI和SSCI论文30余篇。主持国家自然科学基金2项以及教育部人文社科基金和上海市扬帆计划等项目。

报告摘要：In this talk, we are concerned with Unconditional Quantile Regression (UQR) method, which has gained significant traction as a popular approach for modeling and analyzing data. However, much like Conditional Quantile Regression (CQR), UQR encounters computational challenges when it comes to obtaining parameter estimates for streaming datasets. This is attributed to the involvement of unknown parameters in the logistic regression loss function used in UQR, which presents obstacles in both computational execution and theoretical development. To address this, we present a novel approach involving smoothing logistic regression estimation. Subsequently, we propose a renewable estimator tailored for UQR with streaming data, relying exclusively on current data and summary statistics derived from historical data. Theoretically, our proposed estimators exhibit equivalent asymptotic properties to the standard version computed directly on the entire dataset, without any additional constraints. Both simulations and real data analysis are conducted to illustrate the finite sample performance of the proposed methods.