Min Wang, Ph.D.

Professor, Applied Statistics Ph.D. Advisor, Management Science and Statistics

Min Wang Headshot



Min Wang is a professor 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, TEST, 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, Environment, Development and Sustainability, Endocrine, Journal of Cardiovascular Translational Research, Journal of Manufacturing Processes, Medical Physics, and others. To view his up-to-date publications, please visit his Google Scholar profile


  • Bayesian statistics
  • Statistical learning and data mining
  • Predictive modeling

Research Interests

  • Bayesian inference and methods
  • High dimensional inference
  • Prior elicitation
  • Statistical modeling
  • Variable selection


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


  • “Bayesian analysis of testing general hypotheses in linear models with spherically symmetric errors,” with K. Ye and Z. Han, TEST, In print, 2023
  • “On efficient posterior inference in normalized power prior Bayesian analysis,” with Z. Han, Q. Zhang, K. Ye and M. Chen, Biomedical Journal, Vol. 65, No. 5, 2023, pp. 2200194.
  • “Robust Bayesian hierarchical modeling and inference using scale mixtures of normal distributions,” with L. Ouyang, S. Zhu, K. Ye and C. Park, IISE Transactions, Vol. 54 7, 2022, pp. 659-671.
  • “A study on the power parameter in power prior Bayesian analysis,” with Z. Han and K. Ye, The American Statistician, Vol. 77, No. 1, 2023, pp. 12-19.
  • “Mixtures of g-priors for analysis of variance models with a diverging number of parameters,” Bayesian Analysis, Vol. 12, No. 2, 2017, pp. 511-532.
  • “Consistency of Bayes factor for nonnested model selection when the model dimension grows,” with Y. Maruyama, Bernoulli, Vol. 22, No. 4, 2016, pp. 2080-2100.