Research by Associate Prof. Linhan Ouyang that proposed a robust Bayesian modeling technique to implement variable selection and quality prediction was featured in IISE Transactions on May 2021. As the flagship journal of the Institute of Industrial and Systems Engineers, IISE Transactions publishes original high-quality papers on a wide range of topics of interest to industrial engineers who want to remain current with the state-of-the-art technologies. The refereed journal aims to foster the engineering community by publishing papers with a strong methodological focus motivated by real problems that impact engineering practice and research. “The class of scale mixtures of multivariate normal distributions are incorporated into the construction of robust Bayesian SUR model, which can significantly improve the performance of variable selection and quality prediction”, the authors noted. Abstract is copied below.

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 usually employ a single-response normal model to analyze industrial processes without taking into consideration the high correlations and the non-normality among the response variables, and the problem of variable selection has also not yet been fully investigated within this modeling framework. Failure to account for these issues may result in a misleading prediction model and therefore poor process design. In this article, we propose a robust Bayesian seemingly unrelated regression model to simultaneously analyze multiple-feature systems while accounting for the high correlation, non-normality, and variable selection issues. Additionally, we propose a Markov chain Monte Carlo sampling algorithm to generate posterior samples from the full joint posterior distribution to obtain the robust Bayesian estimates. Simulation experiments are executed to investigate the performance of the proposed Bayesian method which is also illustrated to a laser cladding repair process. The analysis results show that the proposed modeling technique compares favorably with its classical counterpart in the literature.

If you are interested in the research, please read the paper

Linhan Ouyang, Shichao Zhu, Keying Ye, Chanseok Park, Min Wang. Robust Bayesian hierarchical modeling and inference using scale mixtures of normal distributions [J]. IISE Transactions, 2021.

A full version of this article could be viewed at:

https://www.tandfonline.com/doi/abs/10.1080/24725854.2021.1912440