Aixin Tan

Aixin Tan
Ph.D. University of Florida 2009

Associate Professor and Director of Graduate Studies
Department of Statistics and Actuarial Science
University of Iowa
259 Schaeffer Hall
Iowa City, IA 52242

Phone: (319) 335-0821
Fax: (319) 335-3017
E-mail: aixin-tan@uiowa.edu

Research Interests

Markov chain Monte Carlo
Bayesian model selection
Bayesian mixture modeling

Publications

Im, Y., Huang, Y., Tan, A., and Ma, S. (2023) Bayesian finite mixture of regression analysis for cancer based on histopathological image-environmental interactions. Biostatistics.

Im, Y. and Tan, A. (2021) Bayesian subgroup analysis in regression using mixture models. Computational Statistics and Data Analysis.

Jin, R. and Tan, A. (2021) Fast Markov chain Monte Carlo for high-dimensional Bayesian regression models with shrinkage priors. Journal of Computational and Graphical Statistics. (Available online here.)

Liu, R.* and Tan, A.* (2020) Towards Interpretable Automated Machine Learning for STEM Career Prediction. Journal of Educational Data Mining.

Roy, V.*, Tan, A.* and Flegal, J. (2018) Estimating standard errors for importance sampling estimators with multiple Markov chains. Statistica Sinica, 28, 1079-1101. (Available on arXiv.)

Tan, A. and Huang, J. (2016) Bayesian inference for high-dimensional linear regression under mnet priors. The Canadian Journal of Statistics, 44, 180-197. (pdf, supplement)

Qian, X.*, Tan, A.*, Ling, J. J., Nakajima, Y., Wang, W. and Zhang, C. (2016) The Gaussian CLs method for searches of new physics. Nuclear Instruments and Methods in Physics, A., 827, 63-78. (Available online here.)

Tan, A., Doss, H. and Hobert, J.P. (2015) Honest importance sampling with multiple Markov chains. Journal of Computational and Graphical Statistics, 24, 792-826. (pdf)

Ghosh, J.* and Tan, A.* (2015) Sandwich algorithms for Bayesian variable selection. Computational Statistics and Data Analysis, 81, 76-88. (pdf)

Doss, H. and Tan, A. (2014) Estimates and standard errors for ratios of normalizing constants from multiple Markov chains via regeneration. Journal of the Royal Statistical Society, Series B, 76, 683-712. (pdf)

Tan, A., Jones, G. and Hobert, J.P. (2013) On the geometric ergodicity of two-variable Gibbs samplers. Advances in Modern Statistical Theory and Applications: A Festschrift in Honor of Morris L. Eaton (G. L. Jones and X. Shen, eds.) 25-42. IMS Collections Ser. 10. IMS, Beachwood, OH. (pdf)

Qian, X.*, Tan, A.*, Wang, W., Ling, J. J., McKeown, R. D. and Zhang, C. (2012) Statistical Evaluation of Experimental Determinations of Neutrino Mass Hierarchy. Physical Review D, 86, 113011. (Available online here.)

Tan, A. and Hobert, J.P. (2009) Block Gibbs sampling for Bayesian random effects models with improper priors: Convergence and regeneration. Journal of Computational and Graphical Statistics, 18, 861-878. (pdf, appendices)

Hobert, J.P., Tan, A. and Liu, R. (2007) When is Eaton's chain irreducible? Bernoulli, 13, 641-652. (pdf)

Ma, C., Yu, Q., Berg, A., Drost, D., Novaes, E., Fu, G., Yap, J. S., Tan, A., Kirst, M., Cui, Y. and Wu, R. (2007) A Statistical Model for Testing the Pleiotropic Control of Phenotypic Plasticity for a Count Trait. Genetics, 179, 627-636.

* indicates corresponding author, and/or equal contribution as the first author.

Submitted

Jin, R. and Tan, A., Central limit theorems for Markov Chains based on their convergence rates in Wasserstein distance.

Teaching

Service

Education

Last updated by Aixin Tan on 08/13/2020