学术报告：Robust and Efficient Mediation Analysis via Huber Loss

报告时间：6月28日（星期五）下午15:00-16:30

报告地点：沙河校区，二教109

报告人：王文武，曲阜师范大学统计与数据科学学院，教授

报告摘要：Mediation analysis is one of the most popularly used methods in social sciences and related areas. To estimate the indirect effect, the least-squares regression is routinely applied, which is also the most efficient when the errors are normally distributed. In practice, however, real data sets are often non-normally distributed, either heavy-tailed or skewed, so that the leastsquares estimators may behave very badly. To overcome this problem, we propose a robust M-estimation for the indirect effect via a general loss function, with a main focus on the Huber loss which is more slowly varying at large values than the squared loss. We further propose a data-driven procedure to select the optimal tuning constant by minimizing the asymptotic variance of the Huber estimator, which is usually more robust and efficient than the least-squares and least-absolute-deviation estimators. Simulation studies show that our new method performs better than the existing competitors in terms of the mean square error, the type I error rate, and the statistical power. Finally, the usefulness of the proposed method is also illustrated using a real data example.

报告人简介：王文武，曲阜师范大学统计与数据科学学院教授、博士生导师;校统计学研究所副所长，院研究生工作负责人。主要从事非参数统计、稳健统计、统计机器学习与因果效应评估等方面研究工作；在机器学习顶级期刊Journal of Machine Learning Research、Knowledge-Based Systems，统计学权威期刊Statistics in Medicine、Test，心理学权威期刊Structural Equation Modeling和环境顶级期刊Journal of Hazardous Materials等发表论文10余篇。目前，主持国家自然科学基金面上项目1项；参与国家自然科学基金面上项目1项。曾以高级研究助理、博士后研究员、访问学者、特邀演讲人等身份访问香港大学和香港浸会大学，累计工作时间超过48个月。