报告题目: Full-semiparametric-likelihood-based inference for non-ignorable missing data

报告人: 刘玉坤教授

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 marginalnonparametric biased sampling likelihood. We show that the extra marginal likelihood contribution can not only produce efficiency gain but also identify the underlyingmodel 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.

报告时间: 2019年3月25日 10:00--11:00

报告地点: 我院211报告厅

报告人简介: 华东师范大学我院教授、博士生导师，研究领域包括经验似然、密度比模型、病例对照数据分析、捕获再捕获数据分析、有限混合模型、小域估计、非参数和半参数统计。2009年6月在南开大学统计学系获得博士学位。2007年11月到2008年10月作为联合培养博士研究生访问加拿大英属哥伦比亚大学统计系。在包括 Journal of the Royal Statistical Society, Series B，The Annals of Statistics, Journal of American Statistical Association, Biometrika 等统计学顶级期刊发表学术论文30余篇。主持国家自然科学基金面上项目两项，青年基金项目一项。