学术报告
阅读次数:489

题目:Robust Estimation for Longitudinal Data with Covariate Measurement Errors and Outliers

报告人:秦国友 副教授(复旦大学)

地点:瑞安楼609室

时间:2017年12月13日(星期三)13:30-14:30

报告摘要

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.

个人简介

秦国友,副教授,复旦大学公共卫生学院生物统计教研室主任,毕业于华东师范大学统计系,师从著名统计学家朱仲义教授,多次主持国家自然科学基金项目,在Biometrics、Biostatistics等高水平SCI期刊发表论文近50篇。

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