摘要：Shape constrained estimation and inference are becoming quite popular in recent years. The allure is the prospect of obtaining ful

摘要

Shape constrained estimation and inference are becoming quite popular in recent years. The allure is the prospect of obtaining fully automatic nonparametric/semiparametric estimators with no tuning parameters to choose. Some well-used shapes include monotonicity, convexity, log-concavity, S-shape, etc. In this talk, my aim is to illustrate the general idea behind the methodology and demonstrate how this idea can be adapted to a selection of statistical problems, including (multi-dimensional) density estimation, semi/non-parametric regression, time series modelling, among others.

嘉宾介绍

Yining Chen is an associate professor in statistics at the London School of Economics and Political Science. He earned his PhD from the University of Cambridge. His current research interests include change-point detection, semi/non-parametric estimation and computational statistics. He is also interested in the applications in areas such as insurance and medical sciences.

来源：哈尔滨探城记