报告题目：Full-semiparametric-likelihood-based inference for non-ignorable missing data
报 告 人：刘玉坤教授 华东师范大学
报告时间：2019年9月22日上午8：30-9：30
报告地点：数学楼629室
报告摘要：
During the past few decades, missing-data problems have been studied extensively, with a focus on the missing-at-random or ignorable missing case, where the missing data depend only on observable quantities. By contrast, research into missing-not-at-random or non-ignorable missing data problems is quite limited. The main difficulty in solving such problems is that the missing probability and the regression likelihood function are tangled together in the likelihood presentation, and the model parameters may not be identifiable even under strong parametric model assumptions. In this paper we discuss a semiparametric model for non-ignorable missing data, and we propose a maximum full semiparametric likelihood estimation method, which is an efficient combination of the parametric conditional likelihood and the marginal nonparametric biased sampling likelihood. We show that the extra marginal likelihood contribution can not only produce efficiency gain but also identify the underlying model parameters without additional assumptions. Extensive simulations demonstrate the advantage of the proposed method over competing methods. For illustration, the proposed method is applied to two real data sets.
报告人简介：
刘玉坤，南开大学博士， 现任华东师范大学统计学院教授。研究方向是以经验似然为基础的半参数统计理论及其在病例对照研究、捕获再捕获数据、有偏样本等方面的应用，迄今发表SCI科研论文三十余篇. 主持3项国家自然科学基金项目和1项重点项目子课题. 英文统计期刊《Journal of Applied Statistics》和中文核心期刊《应用概率统计》编委，华东师大创办的国内第一份纯英文统计期刊《Statistical Theory and Related Fields》主编助理。
