Degrees

• Ph.D. Clemson University
• M.S. Clemson University
• B.A. Concordia University

About

Min Wang is an Associate Professor of Statistics in the Department of Management Science and Statistics at The University of Texas at San Antonio. He previously worked at Michigan Technological University and Texas Tech University. His main research spans the areas of Bayesian inference and methods, high-dimensional inference, prior elicitation, quantile regression, and statistical modeling, all in both methodological and theoretical perspectives. His work has been appeared or accepted in top-ranked journals, such as Bayesian Analysis, Bernoulli, Computers & Industrial Engineering, IISE Transactions, International Journal of Production Research, Journal of the Operational Research Society, Journal of Statistical Planning and Inference, Naval Research Logistics, The American Statistician, and others. He has also conducted interdisciplinary research with various scholars from biomedical engineering, civil engineering, industrial engineering, mechanical engineering, and other sciences. His collaborative work has been published in prestigious journals, such as Acta Neurochirurgica, Construction and Building Materials, Endocrine, Journal of Manufacturing Processes, Medical physics, and others. Dr. Wang’s research was partially supported by funding from National Institute of Health, National Science Foundation, and other agencies.

Research Interests

• Bayesian Inference and Methods
• High Dimensional Inference
• Prior Elicitation
• Statistical Modeling
• Statistical Applications
• Variable Selection

Recent Publications

• “Simulation optimization using stochastic kriging with robust statistics,” with L. Ouyang, M. Han, Y. Ma and C. Park. Journal of the Operational Research Society, in print, 2022.
• “A study on the power parameter in power prior Bayesian analysis,” with Z. Han and K. Ye, The American Statistician, in print, 2022.
• “A study on estimating the parameter of the truncated geometric distribution,” with C. Park and K. Gou, The American Statistician, in print, 2022.
• “Bayesian variable selection and estimation in quantile regression using a quantile-specific prior,” with M. Dao, S. Ghosh and K. Ye, Computational Statistics, Vol. 37, 2022, pp. 1339-1368.
• “Robust Bayesian hierarchical modeling and inference using scale mixtures of normal distributions,” with L. Ouyang, S. Zhu and K. Ye, IISE Transactions, Vol. 54, 2022, 659-671.
• “Health assessment and prognostics based on higher order hidden semi-Markov models,” with Y. Liao and Y. Xiang, Naval Research Logistics, Vol. 68(2), pp. 259-276.
• “Robust g-type quality control charts for monitoring nonconformities,” with C. Park and L. Ouyang, Computers & Industrial Engineering, Vol. 162, 2021, pp. 107765.
• “Bayesian hierarchical modeling for process optimization,” with L. Ouyang, C. Park, Y. Ma and Y. Ma, International Journal of Production Research, Vol. 59, 2021, pp. 4649-4669.
• “Objective Bayesian testing for the correlation coefficient under divergence-based priors,” with B. Peng, The American Statistician, Vol. 75, 2021, pp. 41-51.