报告摘要：

Empirical models that relate multiple quality features to a set of design variables play a vital role in many industrial process optimization methods. Many of the current modeling methods employ a single-response model to analyze industrial processes without taking into consideration the high correlations among the response variables and may result in a misleading prediction model, and therefore, poor process design. To deal with these issues, we first present a Bayesian hierarchical modelling approach to process optimization based on the seemingly unrelated regression (SUR) models. The proposed approach can estimate a set of predictors to be included in a model based on a Bayesian hierarchical procedure (i.e. model selection) and provide model prediction and optimization based on a Bayesian SUR model (i.e. model estimation). We then propose a robust version of Bayesian SUR models to simultaneously analyze multiple-feature systems while accounting for the high correlation, non-normality, and variable selection issues. Simulation experiments are executed to investigate the performance of the proposed Bayesian method, which is also illustrated by application to a laser cladding repair process. The analysis results show that the proposed modeling technique compares favorably with its classic counterpart 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，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。到目前为止，在各种期刊发表学术论文70多篇，其中最近研究成果发表在Bayesian Analysis，Computational Statistics & Data Analysis，Computers and Industrial Engineering, IISE Transactions, International Journal of Production Research, Journal of the Operational Research Society, Naval Research Logistics, The American Statistician 等。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。

时间：2022年3月25日9:00-10：00

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