Aixin Tan

Aixin Tan
Director of Graduate Studies
Associate Professor
PhD in Statistics, University of Florida, 2009
MS in Statistics, University of Florida, 2005
BS in Probability and Statistics, Peking University, Beijing, China, 2003
(319) 335-0821
259 SH
Research Interests: 
Markov Chain Monte Carlo, Bayesian Statistics, Model Selection

Dr. Tan studied statistics at Peking University and the University of Florida. She is interested in Bayesian modeling and computing.

Selected Professional Memberships

  • International Society for Bayesian Analysis (ISBA), 2015
  • American Statistical Association (ASA), 2010
  • Institute of Mathematical Statistics (IMS), 2010

Selected Publications

  • Roy, V., TAN, A. & Flegal, J. (2018). Estimating standard errors for importance sampling estimators with multiple Markov chains. Statistica Sinica, 28, 1079-1101.
  • TAN, A., Huang, J. (2016). Bayesian inference for high-dimensional linear regression under mnet priors. Canadian Journal of Statistics, 44, 180-197.
  • TAN, A., Doss, H. & Hobert, J. P. (2015). Honest importance sampling with multiple Markov chains. J. Comput. Graph. Statist., 24, 792-826.
  • Ghosh, J., TAN, A. (2015). Sandwich algorithms for Bayesian variable selection. Computational Statistics and Data Analysis, 81, 76-88.
  • Doss, H., TAN, A. (2014). Estimates and standard errors for ratios of normalizing constants from multiple Markov chains via regeneration. J. R. Stat. Soc. Ser. B. Stat. Methodol., 76, 683-712.
  • Qian, X., TAN, A., Wang, W., Ling, J. J., McKeown, R. D. & Zhang, C. (2012). Statistical Evaluation of Experimental Determinations of Neutrino Mass Hierarchy. Physical Review D, 86, 113011.
  • TAN, A., Hobert, J. P. (2009). Block Gibbs sampling for Bayesian random effects models with improper priors: Convergence and regeneration. J. Comput. Graph. Statist., 18, 861-878.
  • Hobert, J. P., TAN, A. & Liu, R. (2007). When is Eaton's chain irreducible?. Bernoulli, 13, 641-652.
  • Im, Y., Tan, A. (In Press). Bayesian subgroup analysis in regression using mixture models. Computational Statistics and Data Analysis.
  • Jin, R., Tan, A. (2021). Fast MCMC for high dimensional Bayesian regression models with shrinkage priors. Journal of Computational and Graphical Statistics.