報告題目：
Robust Estimation for Longitudinal Data with Covariate Measurement Errors and Outliers
針對協變量帶有測量誤差和異常值的縱向數據的穩健估計
報告時間：2019年4月16日（星期二）上午10:30-11:30
報告地點：必一体育平台二樓會議室
報告人：秦國友，復旦大學公共衛生必一，教授，博士，碩士生導師。
研究方向包括縱向數據分析，半參數模型的穩健推斷🛹，缺失數據分析以及統計方法在公共衛生和醫學中的應用🧏🏻♀️，主要關註腫瘤，慢性病領域的統計方法應用研究。
在國際上，主要和香港大學、美國伊利諾伊大學厄巴納－香檳分校、美國北卡羅來納大學教堂山分校生物統計系🏃➡️、新加坡南洋理工大學物理與數學科學必一等合作研究。在SCI源期刊上發表幾十篇高質量論文🔻，包括一些在國際高引著名雜誌Biometrics, Biostatistics上發表的論文。 2014年度教育部高等學校科學研究優秀成果獎二等獎(第二完成人)，2015年 入選復旦大學“卓學計劃”，擔任中華預防醫學會生物統計專業青年委員會主任委員🤦🏽♂️，中華預防醫學會生物統計專業委員會 委員🦧，中國衛生信息學會統計理論與方法專業委員會委員，擔任《中國衛生統計》,《復旦學報（醫學版）》編委🥳，《美國數學評論》評論員👏。主持多項國家自然基金和省部級項目基金🫄🏼。
報告摘要：
Measurement errors and outliers often arise in longitudinal data, ignoring the effects of measurement errors and outliers will lead to seriously biased estimators. Therefore, it is important to take them into account in longitudinal data analysis. In this paper, we develop a robust estimating equation method for analysis of longitudinal data with covariate measurement errors and outliers. Specifically, we eliminate the effects of measurement errors by making use of the independence of replicate measurement errors and correct the bias induced by outliers through centralizing the matrix of error-prone covariates in the estimating equation. The proposed method is easy to implement by using the standard generalized estimating equations algorithms and does not require specifying the distributions of the true covariates, response and measurement error. The asymptotic normality of the proposed estimator is established under some regularity conditions. Extensive simulation studies show that the proposed method does have a good performance in handling measurement errors and outliers. In the end, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition (LEAN) study for illustration.