报告题目：Bayesian hypothesis testing and variable selection in linear models with spherically symmetric error

报告所属学科：管理科学与工程

报告人：Min Wang（美国德州大学圣安东尼奥分校）

报告时间：2022年11月18日 9:00-10:30

报告地点：腾讯会议 ID：404-710-449

报告摘要：

In this talk, we consider Bayesian analysis for testing the general linear hypotheses in linear models with spherically symmetric errors, which not only include classical linear models as special cases, but also reduce the influence of outliers and result in a robust statistical inference. By appropriately modifying mixtures of g-priors for the regression coefficients under some general linear constraints, we derive closed-form Bayes factors in terms of the ratio of the two Gaussian hypergeometric functions. The proposed Bayes factors rely on the data only through the modified coefficient of determinations of the two models and are shown to be independent of the error distributions, so long as they are spherically symmetric. Moreover, we establish the model selection consistency of the proposed Bayes factors when the number of parameters increases with the sample size. We carry out simulation studies to assess the finite sample performance of the proposed methodology. The presented results extend some existing results of the Bayesian testing procedures in the literature.

报告人简介：

汪敏，美国德州大学圣安东尼奥分校 (University of Texas at San Antonio) 商学院管理科学与统计系副教授（获终身教职），博士生导师。2010年5月于美国克莱姆森大学（Clemson University）获得统计硕士学位；2013年5月于克莱姆森大学大学获得统计博士学位。2013年8月- 2017年12月在美国密歇根理工大学数学科学系工作和在2017年8月破格提前提升为副教授并获得终身任期教授资格；现在在德州大学圣安东尼奥分校从事教学科研工作。近年来，先后参与和主持了美国自然科学基金委（NSF），密歇根交通部，以及美国卫生院（NIH）的研究课题。其研究成果已发表在IISE Transactions, Naval Research Logistics, International Journal of Production Research, Bayesian Analysis, Computer & Industrial Engineering, The American Statistician, Computational Statistics & Data Analysis等国际权威期刊。主要研究方向包括贝叶斯统计；计算统计；统计推断；质量和可靠性工程研究；高维数据分析和统计应用。