Min Wang

Min Wang


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Associate Professor, Mathematical Sciences

  • PhD, Statistics, Clemson University


Min Wang graduated from Concordia University in December 2007 with major in Mathematics and Statistics, and then went on to earn an M.S. and Ph.D. from Clemson University, where his research mainly focused on Bayesian hypothesis testing and variable selection in high dimensional regression analysis. After finishing his Ph.D. in May 2013, Dr. Wang joined the faculty of Michigan Tech University in August 2013 and received early promotion and tenure to Associate Professor in 2017. His teaching interests include Bayesian Statistics, Engineering Statistics, Statistical Programming, and Linear Models. His current research lies broadly in the areas of Bayesian statistics, multivariate analysis, statistical modelling, and quality and reliability engineering, all in the pragmatic and theoretical aspects. 

Areas of Expertise

  • Bayesian Statistics
  • Statistical Computing
  • Bayesian Modeling
  • Statistical Consulting

Recent Publications

  • S. Kang*, G. Liu, H. Qi and M. Wang (2017). Bayesian variance changepoint detection in linear models with symmetric heavy-tailed errors. Computational Economics. In Press. Read More
  • M. Wang (2017). Mixture of g-priors for analysis of variance models with a diverging number of parameters. Bayesian Analysis, 12, 511-532. Read More
  • S. Li* and M. Wang (2017). Bayesian estimation of the generalized lognormal distribution using objective priors. Journal of Statistical Computation and Simulation, 87, 1323-1341. Read More
  • I. Fonkoue, M. Wang and J. Carter (2016). Sympathetic neural reactivity to mental stress in offspring of hypertensive parents: 20 years revisited. American Journal of Physiology - Heart and Circulatory Physiology, 311, 426-432. Read More
  • M. Wang and Y. Maruyama (2016). Consistency of Bayes factor for nonnested model selection when the model dimension grows. Bernoulli, 22, 2080 - 2100. Read More
  • S. Tu, M. Wang and X. Sun (2016). Bayesian analysis of two-piece location–scale models under reference priors with partial information. Computational Statistics & Data Analysis, 96, 133-144. Read More
  • M. Wang and G. Liu (2016). A simple two-sample Bayesian t-test for hypothesis testing. The American Statistician, 70, 195-201. Read More
  • S. Kang*, M. Wang and T. Lu (2015). On the consistency of the objective Bayes factor for the integral priors in the one-way random effects model. Statistics & Probability Letters, 103, 17-23. Read More

Recent Funding

  • M. Wang (PI): Bayesian Inference in Statistics and Statistical Genetics, National Science Foundation, DMS-1719789. 2017 - 2018 for $10,000.
  • A. Minerick (PI), L. Brown (Co-PI) and M. Wang (Co-PI). STTR Phase II: Microdevice for Rapid Blood Typing without Reagents and Hematocrit Determination (PI: R. Minerick subcontract of $305,000 to MTU). National Science Foundation (NSF), 2016-2018 for $750,000.
  • Z. Liu (PI), S. Vitton (Co-PI), M. Wang (Co-PI) and M. Billmire (Co-PI). Develop and Implement a Freeze Thaw Model Based Seasonal Load Restriction Decision Support Tool. Michigan Department of Transportation (MDOT), 2017 - 2019 for $151,376.
  • M. Wang (PI). Workshop on Bayesian Inference in Statistics and Statistical Genetics. IMA PI Grad Conference, Institute for Mathematics and its Applications (IMA), August 2016 for $4,000.

Teaching Experience

  • MA3710 Engineering Statistics
  • MA3740 Stat Programming and Analysis
  • MA3750 Intro to SAS Programming
  • MA5731 Linear Models
  • MA5770 Bayesian Statistics
  • MA5730 Nonparametric Statistics