Associate Professor of Statistics and Actuarial Science, College of Liberal Arts and Sciences
Associate Professor and Director of Graduate Studies, Department of Statistics and Actuarial Science, University of Iowa, July 2020 - Present
Primary Office: 259 Schaeffer Hall (SH)
Wednesday: 1:45 pm - 3:15 pm
Thursday: 1:45 pm - 3:15 pm
Dr. Tan studied statistics at Peking University and the University of Florida. She is interested in Bayesian modeling and computing.
- PhD in Statistics, University of Florida, 2009
- MS in Statistics, University of Florida, 2005
- BS in Probability and Statistics, Peking University, Beijing, China, 2003
Selected Professional Memberships
- International Society for Bayesian Analysis (ISBA), 2015
- American Statistical Association (ASA), 2010
- Institute of Mathematical Statistics (IMS), 2010
- (2018). Estimating standard errors for importance sampling estimators with multiple Markov chains. Statistica Sinica, 28, 1079-1101.
- (2016). Bayesian inference for high-dimensional linear regression under mnet priors. Canadian Journal of Statistics, 44, 180-197.
- (2015). Honest importance sampling with multiple Markov chains. J. Comput. Graph. Statist., 24, 792-826.
- (2015). Sandwich algorithms for Bayesian variable selection. Computational Statistics and Data Analysis, 81, 76-88.
- (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.
- (2012). Statistical Evaluation of Experimental Determinations of Neutrino Mass Hierarchy. Physical Review D, 86, 113011.
- (2009). Block Gibbs sampling for Bayesian random effects models with improper priors: Convergence and regeneration. J. Comput. Graph. Statist., 18, 861-878.
- (2007). When is Eaton’s chain irreducible?. Bernoulli, 13, 641-652.
- (2021). Fast MCMC for high dimensional Bayesian regression models with shrinkage priors. Journal of Computational and Graphical Statistics.
- (2020). Towards Interpretable Automated Machine Learning for STEM Career Prediction. Journal of Educational Data Mining. DOI: https://doi.org/10.5281/zenodo.4008073.
Last Modified Date: March 8, 2021