Ray Bai
Ray Bai
Assistant Professor of Statistics, University of South Carolina
Verified email at mailbox.sc.edu - Homepage
Cited by
Cited by
Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A Comparative study
I Duerr, HR Merrill, C Wang, R Bai, M Boyer, MD Dukes, N Bliznyuk
Environmental Modelling & Software 102, 29-38, 2018
Spike-and-slab group lassos for grouped regression and sparse generalized additive models
R Bai, GE Moran, JL Antonelli, Y Chen, MR Boland
Journal of the American Statistical Association, 1-14, 2020
High-dimensional multivariate posterior consistency under global–local shrinkage priors
R Bai, M Ghosh
Journal of Multivariate Analysis 167, 157-170, 2018
Large-Scale Multiple Hypothesis Testing with the Normal-Beta Prime Prior
R Bai, M Ghosh
Statistics 53, 1210-1233, 2019
Fast Algorithms and Theory for High-Dimensional Bayesian Varying Coefficient Models
R Bai, MR Boland, Y Chen
arXiv preprint arXiv 1907.06477, 2019
On the Beta Prime Prior for Scale Parameters in High-Dimensional Bayesian Regression Models
R Bai, M Ghosh
Statistica Sinica 31, 843-865, 2021
VCBART: Bayesian trees for varying coefficients
SK Deshpande, R Bai, C Balocchi, JE Starling, J Weiss
arXiv preprint arXiv:2003.06416, 2020
Spike-and-Slab Meets LASSO: A Review of the Spike-and-Slab LASSO
R Bai, V Rockova, EI George
arXiv preprint arXiv:2010.06451, 2020
A Robust Bayesian Copas Selection Model for Quantifying and Correcting Publication Bias
R Bai, L Lin, MR Boland, Y Chen
arXiv preprint arXiv:2005.02930, 2020
Individual-Level and Neighborhood-Level Risk Factors for Severe Maternal Morbidity
JR Meeker, SP Canelón, R Bai, LD Levine, MR Boland
Obstetrics & Gynecology 137, 847-854, 2021
A Unified Computational and Theoretical Framework for High-Dimensional Bayesian Additive Models
R Bai
arXiv preprint arXiv:2007.07021, 2020
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