Semi-parametric method for non-ignorable missing in longitudinal data using refreshment samples

发布时间:2017-06-21浏览次数:146



讲座名称

Semi-parametric method for non-ignorable missing in longitudinal data using refreshment samples

薛兰

举办单位

统计与信息学院

举办地点

博识楼434

举办时间

201762310:00

讲座内容简介



Missing data is one of the major methodological problems in longitudinal studies. It not only reduces the sample size, but also can result in biased estimation and inference. It is crucial to correctly understand the missing mechanism and appropriately incorporate it into the estimation and inference procedures. Traditional methods, such as the complete case analysis and imputation methods, are designed to deal with missing data under unverifiable assumptions of MCAR and MAR.

The purpose of this talk is to identify and estimate missing mechanism parameters under the non-ignorable missing assumption utilizing the refreshment sample. In particular, we propose a semi-parametric method to estimate the missing mechanism parameters by comparing the marginal density estimator using Hirano’s two constraints (Hirano et al. 1998) along with additional information from the refreshment sample.

Asymptotic properties of semi-parametric estimators are developed. Inference based on bootstrapping is proposed and verified through simulations.



主讲人简介


薛兰, 俄勒冈州立大学统计系副教授,博士生导师。2000年毕业中科大统计系,获统计学学士;2005年毕业于密西根州立大学,获统计学博士。2005年进入俄勒冈州立大学统计系工作至今。