Holder： Linglong Kong（University of Alberta)
Time：2025-04-03 14:00-15:00
Location：Siyuan Lecture Hall，Zhi Hua Building-225
Abstract:
In this paper, we tackle the problem of conducting valid statistical inference for quantile regression with streaming data. The main difficulties are that the quantile regression loss function is non-smooth and it is often infeasible to store the entire dataset in memory, rendering traditional methodologies ineffective. We introduce a fully online updating method for statistical inference in smoothed quantile regression with streaming data to overcome these issues. Our main contributions are twofold. First, for low-dimensional data, we present an incremental updating algorithm to obtain the smoothed quantile regression estimator with the streaming data set. The proposed estimator allows us to construct asymptotically exact statistical inference procedures. Second, within the realm of high-dimensional data, we develop an online debiased lasso procedure to accommodate the special sparse structure of streaming data. The proposed online debiased approach is updated with only the current data and summary statistics of historical data and corrects an approximation error term from online updating with streaming data. Furthermore, theoretical results such as estimation consistency and asymptotic normality are established to justify its validity in both settings. Our findings are supported by simulation studies and illustrated through applications to Seoul's bike-sharing demand data and index fund data.
About the Speaker:
Dr. Linglong Kong is a professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, holding a Canada Research Chair in Statistical Learning and a Canada CIFAR AI Chair. He is a Fellow of the American Statistical Association (ASA) and the Alberta Machine Intelligence Institute (Amii) with over 120 peer-reviewed publications in top journals like the Annals of Statistics (AOS), Journal of the American Statistical Association (JASA), and Journal of the Royal Statistical Society Series B (JRSSB), as well as conferences such as NeurIPS, ICML, and ICLR. Dr. Kong’s research is widely recognized. He serves as Associate Editor for the Journal of the American Statistical Association, The Annals of Applied Statistics, The Canadian Journal of Statistics, and Statistics and Its Interface. Additionally, he was a member of the Executive Committee of the Western North American Region of the International Biometric Society, chair of the ASA Statistical Computing Session Program, and chair of the ASA Webinar Committee. His prior roles include serving as Guest Editor for The Canadian Journal of Statistics and Statistics and Its Interface, Associate Editor for the International Journal of Imaging Systems and Technology, Guest Associate Editor for Frontiers in Neuroscience, chair of the ASA Statistical Imaging Session, and member of the Board of Directors for the Statistical Society of Canada. Dr. Kong’s research interests include high-dimensional and neuroimaging data analysis, statistical machine learning, robust statistics, quantile regression, trustworthy machine learning, and artificial intelligence for smart health.
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