講座編號(hào)：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(cái)經(jīng)大學(xué)
講座時(shí)間：2022年10月27日（星期四）下午14:00
講座地點(diǎn)：騰訊會(huì)議，會(huì)議ID：866 154 950
參加對(duì)象：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院全體教師、研究生
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院、研究生院
主講人簡(jiǎn)介：
馮興東，上海財(cái)經(jīng)大學(xué)統(tǒng)計(jì)與管理學(xué)院院長(zhǎng)、統(tǒng)計(jì)學(xué)教授、博士生導(dǎo)師。研究領(lǐng)域?yàn)閿?shù)據(jù)降維、穩(wěn)健方法、分位數(shù)回歸以及在經(jīng)濟(jì)問(wèn)題中的應(yīng)用、大數(shù)據(jù)統(tǒng)計(jì)計(jì)算、強(qiáng)化學(xué)習(xí)等，在國(guó)際頂級(jí)統(tǒng)計(jì)學(xué)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(huì)NeurIPS上發(fā)表論文多篇。2018年入選國(guó)際統(tǒng)計(jì)學(xué)會(huì)推選會(huì)員(Elected member)，2019年擔(dān)任全國(guó)青年統(tǒng)計(jì)學(xué)家協(xié)會(huì)副會(huì)長(zhǎng)以及全國(guó)統(tǒng)計(jì)教材編審委員會(huì)第七屆委員會(huì)專(zhuān)業(yè)委員（數(shù)據(jù)科學(xué)與大數(shù)據(jù)技術(shù)應(yīng)用組），2020年擔(dān)任第八屆國(guó)務(wù)院學(xué)科評(píng)議組（統(tǒng)計(jì)學(xué)）成員，2022年擔(dān)任全國(guó)應(yīng)用統(tǒng)計(jì)專(zhuān)業(yè)碩士教指委委員，兼任國(guó)際統(tǒng)計(jì)學(xué)權(quán)威期刊Annals of Applied Statistics編委（Associate Editor）以及國(guó)內(nèi)統(tǒng)計(jì)學(xué)權(quán)威期刊《統(tǒng)計(jì)研究》編委。
主講內(nèi)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.