講座題目：Bayesian Jackknife Empirical Likelihood-based Inference for Missing Data and Causal Inference

講座時(shí)間：2024年6月12日（周三）14：30--15：30

講座地點(diǎn)：犀浦校區(qū)3號(hào)教學(xué)樓30425

主講人簡(jiǎn)介：趙亦川教授是美國(guó)佐治亞州立大學(xué)的教授，主要研究方向?yàn)樯娣治?、?jīng)驗(yàn)似然方法、非參數(shù)統(tǒng)計(jì)、ROC曲線(xiàn)分析、生物信息學(xué)、蒙特卡洛方法和模糊系統(tǒng)等統(tǒng)計(jì)模型。趙教授在廣泛的統(tǒng)計(jì)學(xué)和生物統(tǒng)計(jì)學(xué)研究領(lǐng)域發(fā)表了一百多篇研究論文，在施普林格出版社編輯出版六本書(shū)籍，在全球各地作了兩百多次的學(xué)術(shù)報(bào)告，多次成功舉辦了統(tǒng)計(jì)學(xué)，生物統(tǒng)計(jì)學(xué)和生物信息學(xué)方面的大型國(guó)際學(xué)術(shù)會(huì)議。趙教授目前是若干權(quán)威統(tǒng)計(jì)期刊的付主編或編委會(huì)成員，是美國(guó)統(tǒng)計(jì)學(xué)會(huì)的會(huì)士和國(guó)際統(tǒng)計(jì)學(xué)會(huì)的當(dāng)選成員。

講座內(nèi)容簡(jiǎn)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.