题目：From Shape-Constrained Regression to Feature Detection

主讲人：伦敦政治经济学院 陈一宁助理教授

主持人：统计学院 常晋源教授

时间：2022年1月4日（周二）晚上19:30-20:30

地点：腾讯会议，244 774 905

报告摘要：

In this talk, I will first revisit some of the commonly-used tools in shape-constrained regression problems, where shapes we consider including isotonic, unimodal, convex/concave regression. I will also talk about more recent work on S-shape regression, where the regression function consists of both convex and concave parts. In the second part, I shall discuss how these estimators provide natural candidates for estimating the of features of the regression function, such as the mode and the inflection point. Finally, I will discuss how these tools can be used together with techniques from the change-point detection literature to detect useful (potentially multiple) features of a regression function. (This talk is based on joint work with Oliver Feng, Qiyang Han, Ray Carroll, Richard Samworth and Piotr Fryzlewicz.)

主讲人简介：

Yining Chen is an assistant professor in statistics at the London School of Economics; he did his PhD at the University of Cambridge; his current research interests include non-parametric (especially shape-constrained) problems, change-point detection and statistical computing.