主 题: A Framework for Distribution-Free Regression
报告人: Jing Lei (Carnegie Mellon University)
时 间: 2017-06-27 14:00-15:00
地 点: 理科一号楼1114
Abstract: Standard theory in regression analysis has been plagued by the vulnerability of stringent model assumptions in the high dimensional setting, and the resulting inference often fails to take into account the modeling error. I will introduce a new inference framework for regression analysis, by combining the nonparametric rank and order statistics with recent advances in online learning theory. The proposed method is a generic tool that converts any point estimator to an interval predictor, producing prediction bands with valid average coverage under essentially no assumptions, while retaining the optimality of the initial point estimator under standard assumptions. The generality and flexibility of this framework will be illustrated through several topics in regression analysis, including in-sample prediction, variable selection, and prediction bands with adaptive local width. Collaborators: Larry Wasserman, Ryan Tibshirani, Alessandro Rinaldo, and Max G’Sell.
About the speaker: Jing Lei obtained B.S. in Probability and Statistics from the School of Mathematical Sciences at Peking University in 2005, and Ph.D. in Statistics at UC Berkeley in 2010. He is now Associate Professor of Statistics at Carnegie Mellon University. His research interests include statistical machine learning, data privacy, and bioinformatics. He received the 2016 NSF CAREER Award and 2016 ASA Noether Young Scholar Award.
