講座編號：jz-yjsb-2022-y045

講座題目：Quantile Regression for Nonignorable Missing Data with Its Application of Analyzing Electronic Medical Records

主 講 人：馮興東 教授 上海財經大學

講座時間：2022年10月27日（星期四）下午14:00

講座地點：騰訊會議，會議ID：866 154 950

參加對象：數學與統計學院全體教師、研究生

主辦單位：數學與統計學院、研究生院

主講人簡介：

馮興東，上海財經大學統計與管理學院院長、統計學教授、博士生導師。研究領域為數據降維、穩健方法、分位數回歸以及在經濟問題中的應用、大數據統計計算、強化學習等，在國際頂級統計學期刊Journal of the American Statistical Association、Annals of Statistics、Journal of the Royal Statistical Society-Series B、Biometrika以及人工智能頂會NeurIPS上發表論文多篇。2018年入選國際統計學會推選會員(Elected member)，2019年擔任全國青年統計學家協會副會長以及全國統計教材編審委員會第七屆委員會專業委員（數據科學與大數據技術應用組），2020年擔任第八屆國務院學科評議組（統計學）成員，2022年擔任全國應用統計專業碩士教指委委員，兼任國際統計學權威期刊Annals of Applied Statistics編委（Associate Editor）以及國內統計學權威期刊《統計研究》編委。

主講內容：

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.