主题：Sequential quantile regression for streaming data by least squares
主讲人：范烨 首都经济贸易大学
主持人：王国长 威尼斯欢迎你welcome
时间：2024年11月10日（周日）上午9:30-10:30
地点：威尼斯欢迎你welcome石牌校区威尼斯欢迎你welcome大楼（中惠楼）102室
摘要
Massive streaming data are common in modern economics applications, such as e-commerce and finance. They cannot be permanently stored due to storage limitation, and real-time analysis needs to be updated frequently as new data become available. In this work, we develop a sequential algorithm, SQR, to support efficient quantile regression (QR) analysis for streaming data. Due to the non-smoothness of the check loss, popular gradient-based methods do not directly apply. Our proposed algorithm, partly motivated by the Bayesian QR, converts the non-smooth optimization into a least squares problem and is hence significantly faster than existing algorithms that all require solving a linear programming problem in local processing. We further extend the SQR algorithm to composite quantile regression (CQR), and prove that the SQR estimator is unbiased, asymptotically normal and enjoys a linear convergence rate under mild conditions. We also demonstrate the estimation and inferential performance of SQR through simulation experiments and a real data example on a US used car price data set.
主讲人简介
范烨，首都经济贸易大学统计学院讲师。主要研究方向为分位数回归、大数据分布式计算、流数据在线推断等。目前在Journal of Econometrics，Journal of Computational and Graphical Statistics，Data Mining and Knowledge Discovery，Computational Statistics & Data Analysis等学术期刊发表多篇论文，主持教育部人文社科青年基金项目一项。
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校对| 王国长
责编| 彭 毅
初审| 姜云卢
终审发布| 何凌云
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