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Changbao Wu: Doubly Robust Inference with Non-probability Survey Samples
发布日期:2018-07-05  字号:   【打印

报告时间2018年7月6日(星期五)9:30-12:00

报告地点:翡翠湖校区科教楼B座1710室

  Changbao Wu教授

工作单位Department of Statistics and Actuarial Science,University of Waterloo

举办单位:数学学院

报告人简介

伍昌保,加拿大滑铁卢大学统计与精算系教授,负责科研的系副主任。1999年在西蒙弗雷泽大学获统计学博士学位,主要研究领域为抽样调查及复杂数据分析。美国统计学会会士(Fellow of ASA),国际统计学会当选会员(Elected Member of ISI),现为Biometrika,JASA, Journal of Nonparametric Statistics and Survey Methodology副主编。

报告简介

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. The proposed method is used to analyze a non-probability survey sample collected by the Pew Research Center with auxiliary information from the Behavioral Risk Factor Surveillance System and the Current Population Survey. 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.

(王青山/文)  
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