† = student advised by Dr. Bai
§ = cofirst author
* = alphabetical order
Semiparametric/Nonparametric Estimation and Variable Selection
 Bai, R.^{§}, Moran, G. E.^{§}, Antonelli, J. L.^{§}, Chen, Y., and Boland, M. R. (2021). Spikeandslab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association (in press).
[Paper] [Supplement] [Software] 
Bai, R. (2021+). Spikeandslab group lasso for consistent estimation and variable selection in nonGaussian generalized additive models. Working paper.
[Preprint] [Software]
HighDimensional Statistics

Bai, R., Ročková, V., and George, E. I. (2022). Spikeandslab meets LASSO: A review of the spikeandslab LASSO. Handbook of Bayesian Variable Selection (Tadesse, M. and Vannucci, M., eds), Chapman & Hall/CRC Press (in press).
[Paper] [Software] 
Bai, R. and Ghosh, M. (2021). On the beta prime prior for scale parameters in highdimensional Bayesian regression models. Statistica Sinica, 31: 843865.
[Paper] [Supplement] [Software] 
Bai, R. and Ghosh, M. (2019). Largescale multiple hypothesis testing with the normalbeta prime prior. Statistics, 53: 12101233.
[Paper] [Supplement] [Software] 
Bai, R. and Ghosh, M. (2018). Highdimensional multivariate posterior consistency under globallocal shrinkage priors. Journal of Multivariate Analysis, 167: 157170.
[Paper] [Supplement] [Software]
Varying Coefficient and Time Series Models

Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E., and Weiss, J. (2021+). VCBART: Bayesian trees for varying coefficients. Under review.
[Preprint] [Software]  Bai, R., Boland, M. R., and Chen, Y. (2021+). Fast algorithms and theory for highdimensional Bayesian varying coefficient models. Under review.
[Preprint] [Software]
MetaAnalysis

Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2021+). A robust Bayesian Copas selection model for quantifying and correcting publication bias. Under revision.
[Preprint] [Software] 
Bai, R.^{§}, Liu, X.^{§}, Lin, L., Liu, Y., Kimmel, S. E., Chu, H., and Chen, Y. (2021+). A Bayesian selection model for correcting outcome reporting bias with application to a metaanalysis on heart failure interventions. Under review.
[Preprint] [Software]
Spatiotemporal Modeling and NeighborhoodLevel Analysis
 Meeker, J. R., Burris, H. H., Bai, R., Levine, L. D., and Boland, M. R. (2021). Neighborhood deprivation increases the risk of postinduction cesarean delivery. Journal of the American Medical Informatics Association (in press).

Meeker, J. R., Canelón, S. P., Bai, R., Levine, L. D., and Boland, M. R. (2021). Individual and neighborhoodlevel risk factors for severe maternal morbidity. Obstetrics & Gynecology, 137: 847854.
[Paper] 
Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mellis, I., Mowery, D. L., and Herman, D. (2021). Association of neighborhoodlevel factors and COVID19 infection patterns in Philadelphia using spatial regression. AMIA Annual Symposium Proceedings, 545554.
[Paper]  Duerr, I., Merrill, H. R., Wang, C., Bai, R., Boyer, M. J., Dukes, M. D., and Bliznyuk, N. (2018). Forecasting urban water demand with statistical and machine learning methods using large spacetime data. Environmental Modeling and Software, 102: 2938.
[Paper] 
Balocchi, C.^{§}, Bai, R.^{§}, Liu, J., Canelón, S. P., George, E. I., Chen, Y., and Boland, M. R. (2021+). A Bayesian hierarchical modeling framework for geospatial analysis of adverse pregnancy outcomes. Under revision.
[Preprint] [Software]