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  • 统计与管理学院2017年学术报告第21

     

    【主  题】Doubly Robust Estimation of Partially Linear Models for Longitudinal Data with Dropouts and Measurement Error in Covariates

    【报告人】秦国友, 副研究员

    复旦大学公共卫生学院

    【时  间】 2017年05月03日(星期三)15:00-16:00

    【地  点】 上海财经大学统计与管理学院大楼1208室

    【摘  要】In longitudinal studies, missing responses and mis-measured covariates are commonly seen due to the data collection process. Without cautiousness in data analysis, inferences from the standard statistical approaches may lead to wrong conclusions. In order to improve the estimation for longitudinal data analysis, a doubly robust estimation method for partially linear models, which can simultaneously account for the missing responses and mis-measured covariates, is proposed. Imprecisions of covariates are corrected by taking advantage of the independence between replicate measurement errors, rather than by making extra postulations on the distribution of the mis-measured covariates and missing responses are handled by the doubly robust estimation under the mechanism of missing at random (MAR). The asymptotic properties of the proposed estimators are established under regularity conditions, and simulation studies demonstrate desired properties. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition (LEAN) study.

    【邀请人】 柏杨、黄涛