报告人：冯兴东
报告地点：腾讯会议ID:969-241-575
报告时间：2022年11月30日星期三13:30-14:10
报告摘要:
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.
主讲人简介：
博士毕业于美国伊利诺伊大学香槟分校，现任上海财经大学统计与管理学院院长、统计学教授、博士生导师。在国际顶级统计学期刊JASA、AoS、JRSSB、Biometrika以及人工智能顶会NeurIPS上发表论文多篇。2018年入选国际统计学会推选会员(Elected member)，2019年全国青年统计学家协会副会长，2019年全国统计教材编审委员会第七届委员会专业委员（数据科学与大数据技术应用组），2020年担任国务院学科评议组（统计学）成员，2022年担任应用统计专业硕士教指委委员，国内权威期刊《统计研究》编委和国际统计学权威期刊《Annals of Applied Statistics》编委。
