§ = 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. Revision submitted.
[Preprint] [Software] 
Bai, R., Jeong, S., and Ročková, V. (2021+). Minimax rates and adaptive procedures for generalized nonparametric regression. In preparation.
HighDimensional Statistics

Bai, R., Ročková, V., and George, E. I. (2021+). 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]
Bai, R. (2021+). Uncertainty quantification for Bayesian vector autoregressive models. In preparation.
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., Chu, H., and Chen, Y. (2021+). ABSORB: A Bayesian Selection model for correcting and quantifying Outcome Reporting Bias in multivariate metaanalysis. In preparation.
Spatiotemporal Modeling and NeighborhoodLevel Analysis

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). A method to link neighborhoodlevel covariates to COVID19 infection patterns in Philadelphia using spatial regression. AMIA 2021 Virtual Informatics Summit, 2021 Mar 24.
[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]
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. Revision submitted.

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]
Theoretical Analysis of Algorithms

Bai, R.^{†} and Qin, Q.^{†} (2021+). Analysis of MCMC algorithms for Gaussian process regression with automatic relevance determination kernels. In preparation.