报告题目：Biased-sample empirical likelihood weighting for missing data problems: an alternative to inverse probability weighting
报 告 人：刘玉坤 (华东师范大学 教授)
报告时间：2023-10-31(星期二) 10:00开始
报告地点：腾讯会议号：275-637-022
报告摘要：Inverse probability weighting (IPW) is widely used in many areas when data are subject to unrepresentativeness, missingness, or selection bias. An inevitable challenge with the use of IPW is that the IPW estimator can be remarkably unstable if some probabilities are very close to zero. To overcome this problem, at least three remedies have been developed in the literature: stabilizing, thresholding, and trimming. However, the final estimators are still IPW-type estimators, and inevitably inherit certain weaknesses of the naive IPW estimator: they may still be unstable or biased. We propose a biased-sample empirical likelihood weighting (ELW) method to serve the same general purpose as IPW, while completely overcoming the instability of IPW-type estimators by circumventing the use of inverse probabilities. The ELW weights are always well defined and easy to implement. We show theoretically that the ELW estimator is asymptotically normal and more efficient than the IPW estimator and its stabilized version for missing data problems. Our simulation results and a real data analysis indicate that the ELW estimator is shift-equivariant, nearly unbiased, and usually outperforms the IPW-type estimators in terms of mean square error.
专家简介：刘玉坤，华东师范大学 经济与管理学部 统计学院教授，统计交叉科学研究院副院长，统计学院院长助理；入选国家高层次青年人才计划（教育部）。本科和博士毕业于南开大学统计学系，曾经访问加拿大不列颠哥伦比亚大学、滑铁卢大学以及香港大学。主要研究方向经验似然和半参数统计理论、缺失数据和因果推断、统计（与生物、生态、经济等领域）交叉研究、大数据分析以及统计机器学习。迄今为止在统计学顶级期刊《Journal of American Statistical Association》、《Annals of Statistics》、《Journal of the Royal Statistical Society, Series B》、《Biometrika》、《Biometrics》等SCI检索的国际期刊上发表论文20多篇。主持科技部国家重点研发计划课题1项、国家自然科学基金项目4项。长期担任统计期刊“Statistical Theory and Related Fields”的主编、“Journal of Applied Statistics”的副主编以及“应用概率统计”的编委。
