讲座题目：Bayesian Jackknife Empirical Likelihood-based Inference for Missing Data and Causal Inference
讲座时间：2024年6月12日（周三）14：30--15：30
讲座地点：犀浦校区3号教学楼30425
主讲人简介：赵亦川教授是美国佐治亚州立大学的教授，主要研究方向为生存分析、经验似然方法、非参数统计、ROC曲线分析、生物信息学、蒙特卡洛方法和模糊系统等统计模型。赵教授在广泛的统计学和生物统计学研究领域发表了一百多篇研究论文，在施普林格出版社编辑出版六本书籍，在全球各地作了两百多次的学术报告，多次成功举办了统计学，生物统计学和生物信息学方面的大型国际学术会议。赵教授目前是若干权威统计期刊的付主编或编委会成员，是美国统计学会的会士和国际统计学会的当选成员。
讲座内容简介：
Missing data reduces the representativeness of the sample and can lead to inference problems. This study applied the Bayesian jackknife empirical likelihood method for inference with missing data that were missing at random and causal inference. The semiparametric fractional imputation estimator, propensity score weighted estimator, and doubly robust estimator were used for constructing the jackknife pseudo values which were needed for conducting Bayesian jackknife empirical likelihood-based inference with missing data. Existing methods, such as normal approximation and jackknife empirical likelihood, were compared with the Bayesian jackknife empirical likelihood approach in a simulation study. The proposed approach had better performance in many scenarios in terms of the behavior of credible intervals. Furthermore, we demonstrated the application of the proposed approach for causal inference problems in a study of risk factors for impaired kidney function.
