报告题目：An Optimal Two-Step Estimation Approach for Two-Phase Studies
报 告 人：黄坚祐 副教授（香港理工大学）
报告时间：2024年12月3日（星期二）14：00-15：00
报告地点：数学楼114（小报告厅）
校内联系人： 王晓光 副教授 联系方式：84708351-8508
报告摘要：Two-phase sampling is commonly adopted for reducing cost and improving estimation efficiency. In this project, we consider the two-phase study design where the outcome and some cheap covariates are observed for a large cohort at Phase I, and expensive covariates are obtained for a selected subset of the cohort at Phase II. As a result, the analysis of the association between the outcome and covariates faces a missing data problem. The complete case analysis that uses only the Phase II sample is generally inefficient. We develop a two-step estimation approach, which first obtains an estimator using the complete data, and then updates it using an asymptotically mean-zero estimator obtained from a working model between the outcome and cheap covariates using the full data. The two-step estimator is asymptotically at least as efficient as the complete-data estimator and is robust to misspecification of the working model. We propose a kernel-based method to construct a two-step estimator that achieves optimal efficiency, and also develop a simple joint update approach based on multiple working models to approximate the optimal estimator. The proposed method is based on the influence function and is generally applicable as long as the complete-data estimator is asymptotically linear. We demonstrate the advantages of the proposed method over the existing approaches via simulation studies and provide applications to real biomedical studies.
报告人简介：黄坚祐，香港理工大学应用数学系副教授，香港大学本科硕士，北卡罗来纳大学教堂山分校生物统计博士。主要从事与高维数据和生存分析相关的研究。目前已在Ann. Statist.、J. Amer. Statist. Assoc.、Biometrics、Statist. Sinica、Stat. Med.、Stat. Methods Med. Res、Biom. J. 等杂志上发表学术论文20多篇。
