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统计机器学习与因果效应评估系列学术报告(5) 圣路易斯华盛顿大学林楠教授报告

发布时间:2024-02-23文章来源: 浏览次数:

报告题目Sure Independence Screening for Mediation Analysis

报告人林楠 教授 圣路易斯华盛顿大学)

报告时间2024年2月27日上午 10:00-11:00

报告地点学院会议室

报告摘要:

In recent years, substantial research effort has been devoted to developing methodology for high dimensional mediation analysis to identify variables from a high-dimensional set to explain the causal mechanism. Traditional screening approaches are often applied, while the linear structural equation model structure of the mediation problem is not well accounted for. We propose a new marginal screening procedure, termed Marginal Sobel Screening (MSS), for high dimensional mediation analysis that takes into account the mediation model structure. We establish sample level properties and population properties to ensure the sure screening property for MSS. MSS is shown via simulation to perform better than benchmark approaches and is applied to the Coronary Artery Risk Development inYoung Adults (CARDIA) Study to examine the mediation effect of ultra-high dimensional DNA methylation markers.

报告人简介:林楠教授1999年毕业于中国科学技术大学少年班系,2003年在美国伊利诺伊大学获得统计学专业博士学位,2003-2004年在耶鲁大学做博士后,2004年至今在圣路易斯华盛顿大学任教。现为统计与数据科学系教授。主要从事大数据统计计算、分位数回归,生物信息学以及相关应用领域的研究工作。先后在Biometrika,Biometrics,JCGS,TKDE,New England Journal of MedicineGenome Research等国际期刊发表70余篇高水平学术论文。曾担任《Computational Statistics & Data Analysis》国际期刊副主编,现任《Journal of Computational and Graphical Statistics》国际期刊副主编。



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