Research interests

• 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.