【主讲人简介】：周文心，伊利诺伊大学芝加哥分校商学院信息与决策科学系终身副教授。其研究方向主要包括高维统计、稳健推断、现代机器学习、非参数与半参数统计、分位数回归、期望短缺回归以及隐私保护统计方法等。周文心博士于2013年获香港科技大学数学博士学位，曾先后在墨尔本大学、普林斯顿大学运筹与金融工程系从事博士后研究，并于2017年至2023年任教于加州大学圣地亚哥分校数学系。其研究工作发表于 Journal of the American Statistical Association、Annals of Statistics、Journal of the Royal Statistical Society: Series B、Journal of Econometrics、Management Science等统计学、计量经济学和管理科学领域重要期刊。

【内容简介】：Rank regression offers robustness to outliers and heavy-tailed response distributions, invariance to monotonic transformations, and improved efficiency under non-Gaussian errors, making it a versatile tool for analyzing complex data. In this talk, we introduce Generalized Rank Regression (GRR), an extension of classical rank-based methods that accommodates non-monotonic score functions. While aimed at enhancing the statistical efficiency of robust estimators, this generalization results in a potentially non-convex and non-smooth objective function, presenting challenges for both theoretical analysis and algorithmic implementation. We derive a non-asymptotic Bahadur representation of the proposed estimator and establish its asymptotic normality under mild conditions. To address the optimization challenges, we propose a new two-stage sub-gradient descent algorithm that enables efficient computation of GRR estimators with desirable statistical properties. Furthermore, we develop a multiplier bootstrap procedure for conducting statistical inference. A close connection between GRR and variants of quantile regression is uncovered, which demonstrates that GRR and composite quantile regression share asymptotically equivalent variances. The advantages of GRR are illustrated through extensive simulation studies and a real data application. This talk is based on a joint work with Jiyuan Tu (SUFE), Suqi Wu (SJTU) and Yichen Zhang (Purdue).

【讲座时间】：2026年6月29日（星期一）下午15:30

【讲座地点】：人文社科科研楼1801会议室