Aixin Tan
Aixin Tan
Department of Statistics and Actuarial Science, University of Iowa
Verified email at - Homepage
Cited by
Cited by
Statistical evaluation of experimental determinations of neutrino mass hierarchy
X Qian, A Tan, W Wang, JJ Ling, RD McKeown, C Zhang
Physical Review D 86 (11), 113011, 2012
The Gaussian CL s method for searches of new physics
X Qian, A Tan, JJ Ling, Y Nakajima, C Zhang
Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2016
Block Gibbs sampling for Bayesian random effects models with improper priors: Convergence and regeneration
A Tan, JP Hobert
Journal of Computational and Graphical Statistics 18 (4), 861-878, 2009
A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait
CX Ma, Q Yu, A Berg, D Drost, E Novaes, G Fu, JS Yap, A Tan, M Kirst, ...
Genetics 179 (1), 627-636, 2008
Estimates and standard errors for ratios of normalizing constants from multiple Markov chains via regeneration
H Doss, A Tan
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2014
On the geometric ergodicity of two-variable Gibbs samplers
A Tan, GL Jones, JP Hobert
Advances in Modern Statistical Theory and Applications: A Festschrift in …, 2013
Honest importance sampling with multiple Markov chains
A Tan, H Doss, JP Hobert
Journal of Computational and Graphical Statistics 24 (3), 792-826, 2015
Estimating standard errors for importance sampling estimators with multiple Markov chains
V Roy, A Tan, JM Flegal
Statistica Sinica, 1079-1101, 2018
When is Eaton’s Markov chain irreducible?
JP Hobert, A Tan, R Liu
Bernoulli 13 (3), 641-652, 2007
Bayesian inference for high‐dimensional linear regression under mnet priors
A Tan, J Huang
Canadian Journal of Statistics 44 (2), 180-197, 2016
Sandwich algorithms for Bayesian variable selection
J Ghosh, A Tan
Computational Statistics & Data Analysis 81, 76-88, 2015
Convergence rates and regeneration of the block Gibbs sampler for Bayesian random effects models
A Tan
University of Florida, 2009
Towards interpretable automated machine learning for STEM career prediction
R Liu, A Tan
JEDM| Journal of Educational Data Mining 12 (2), 19-32, 2020
Central limit theorems for Markov chains based on their convergence rates in Wasserstein distance
R Jin, A Tan
arXiv preprint arXiv:2002.09427, 2020
Bayesian subgroup analysis in regression using mixture models
Y Im, A Tan
Computational Statistics & Data Analysis 162, 107252, 2021
Fast Markov Chain Monte Carlo for High-Dimensional Bayesian Regression Models With Shrinkage Priors
R Jin, A Tan
Journal of Computational and Graphical Statistics, 1-15, 2021
S1 Proof of Theorem
V Roy, A Tan, JM Flegal
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