时 间：2021年4月21日（周三）10:00-11:00
地 点：腾讯会议ID：381 639 565
题 目：Statistical inference with non-probability survey samples
主讲人：Pengfei Li Professor ，University of Waterloo
主持人：刘玉坤 教授
摘 要：
We establish a general framework for statistical inferences with non-probability survey samples when relevant auxiliary information is available from a probability survey sample. We develop a rigorous procedure for estimating the propensity scores for units in the non-probability sample, and construct doubly robust estimators for the finite population mean. Variance estimation is discussed under the proposed framework. Results from simulation studies show the robustness and the efficiency of our proposed estimators as compared to existing methods. Our results illustrate a general approach to inference with non-probability samples and highlight the importance and usefulness of auxiliary information from probability survey samples.
报告人简介：
Dr. Pengfei Li completed his Ph.D in Dec. 2007 from University of Waterloo and postdoctoral studies from University of British Columbia in 2008. He is Professor at University of Waterloo since 2019. Dr. Li has published around 65 papers in refereed journals or books. Fifteen of them have appeared in top statistical journals such as Annals of Statistics, Biometrika, Journal of the American Statistical Association, and Journal of the Royal Statistical Society: Series B. Dr. Li has served as the associate chair for Undergraduate Studies for two years at the University of Waterloo. He has received the Faculty of Mathematics Award for Distinction in Teaching in 2017 and Faculty of Mathematics Golden Jubilee Research Excellence Award in 2020. Dr. Li is currently serving as associate editor of The Canadian Journal of Statistics and Metrika.