Combining Probability and Non-probability Samples Using Semi-parametric Quantile Regression and a Non-parametric Estimator of the Participation Probability

主讲人：Cindy Yu 

Dr. Cindy Yu (于珑) is a Professor in the Department of Statistics at Iowa State University. She earned her Ph.D. in Statistics from Cornell University in 2005 and has been at Iowa State University since then. Her research focuses on financial statistics, missing data analysis, survey statistics, and causal inference. Dr. Yu has published in journals, including the Journal of the American Statistical Association, Review of Financial Studies, Management Science, Mathematical Finance, Bernoulli, Survey Methodology, Statistica Sinica and the American Journal of Agricultural Economics, among others.

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报告信息

时间 

2026年3月9日（周一）

14:00

地点 

中国人民大学中关村校区

明德主楼1016

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报告摘要

Non-probability samples are prevalent in various fields, such as biomedical studies, educational research, and business investigations, owing to the escalating challenges associated with declining response rates and the cost-effectiveness and convenience of utilizing such samples. However, relying on naive estimates derived from non-probability samples, without adequate adjustments, may introduce bias into study outcomes. Addressing this concern, data integration methodologies, which amalgamate information from both probability and non-probability samples, have demonstrated effectiveness in mitigating selection bias. Commonly employed data integration approaches encompass mass imputation, propensity score weighting, and hybrid methodologies. Nonetheless, the efficacy of these methods hinges upon the assumptions underlying the models. This paper introduces innovative and robust data integration approaches, notably a semi-parametric quantile regression-based mass imputation approach and a doubly robust approach that integrates a non-parametric estimator of the participation probability for non-probability samples. Our proposed methodologies exhibit greater robustness compared to existing parametric approaches, particularly concerning model misspecification and outliers. Theoretical results are established, including variance estimators for our proposed estimators. Through comprehensive simulation studies and real-world applications, our findings demonstrate the promising performance of the proposed estimators in reducing selection bias and facilitating valid statistical inference. This research contributes to the advancement of robust methodologies for handling non-probability samples, thereby enhancing the reliability and validity of research outcomes across diverse domains. This is a joint work with Emily Berg and Sixia Chen.