English
讲座编号：jz-yjsb-2022-y045
讲座问题：Quantile Regression for Nonignorable Missing Data with Its Application of Analyzing Electronic Medical Records
主 讲 人：冯兴东 教授 上海财经大学
讲座时间：2022年10月27日（星期四）下午14:00
讲座所在：腾讯聚会，聚会ID：866 154 950
加入工具：数学与统计学院全体西席、研究生
主理单位：数学与统计学院、研究生院
主讲人简介：
冯兴东，上海财经大学统计与治理学院院长、统计学教授、博士生导师。研究领域为数据降维、稳健要领、分位数回归以及在经济问题中的应用、大数据统计盘算、强化学习等，在国际顶级统计学期刊Journal of the American Statistical Association、Annals of Statistics、Journal of the Royal Statistical Society-Series B、Biometrika以及人工智能顶会NeurIPS上揭晓论文多篇。2018年入选国际统计学会推选会员(Elected member)，2019年担当天下青年统计学家协会副会长以及天下统计课本编审委员会第七届委员会专业委员（数据科学与大数据手艺应用组），2020年担当第八届国务院学科评议组（统计学）成员，2022年担当天下应用统计专业硕士教指委委员，兼任国际统计学权威期刊Annals of Applied Statistics编委（Associate Editor）以及海内统计学权威期刊《统计研究》编委。
主讲内容：
Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existing literature.
