Robust and efficient estimation for the treatment effect in causal inference and missing data problems
报 告 人:: 林华珍
报告地点:: 综合教学楼106室
报告时间:: 2018年08月14日星期二10:00-11:00

   The Mann-Whitney statistic based on complete data only might be invalid if the outcome variables are missing  due to certain covariates. In this paper, we used the probabilistic index modelling to obtain a new Mann-Whitney statistic when missingness occurs in the outcomes but multiple explanatory variables are observable. Our method combined the efficiency of the model-based approach and the robustness of the nonparametric approach. It requires few model assumptions and is shown to be efficient if all specifications are correct, and doubly robust if some part is misspecified. Results from simulation studies and a real data analysis of consumer phone loans in China are presented to demonstrate the advantages of the proposed method over other methods.

发 布 人:吴双 发布时间: 2018-08-13
林华珍,西南财经大学统计学院教授、博导,统计研究中心主任,美国华盛顿大学生物统计系博士后,四川大学博士。教育部长江学者特聘教授,国家杰出青年科学基金获得者,百千万人才工程国家级人选,教育部新世纪优秀人才,第十一批四川省学术和技术带头人。 论文发表在JASA、Annals of Statistics、JRSSB、Biometrika、Journal of Econometrics及Biometrcs等国际统计学及计量经济学顶级期刊上,并先后担任国际统计学期刊《Biometrics》、《Scandinavian Journal of Statistics》、《Statistics and Its Interface》、《Statistical Theory and Related Fields》Associate Editor, 国内核心学术期刊《应用概率统计》、《系统科学与数学》、《数理统计与管理》编委。 研究领域:非参数理论和方法、转换模型、生存数据分析、函数型数据分析、时空数据分析。