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( _____ = student author, * = cofirst author)
HighDimensional Statistics
 Bai, R.^{*}, Moran, G. E.^{*}, Antonelli, J. L.^{*}, Chen, Y., and Boland, M. R. (2022). Spikeandslab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association, 117: 184197.
[Paper] [Supplement] [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., Ročková, V., and George, E. I. (2021). Spikeandslab meets LASSO: A review of the spikeandslab LASSO. In Tadesse, M. G. and Vannucci, M. (Eds.), Handbook of Bayesian Variable Selection, 81108. Chapman & Hall/CRC Press.
[Paper] [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  Corrigendum] [Software]

Wang, S.H., Bai, R., and Huang, H. H. (2023+). Twostep mixedtype multivariate Bayesian sparse variable selection with shrinkage priors.
[Preprint] [Software]

Bai, R. (2023+). Bayesian group regularization in generalized linear models with a continuous spikeandslab prior.
[Preprint] [Software]

Zgodic, A., Bai, R., Zhang, J., Wang, Y., Rorden, C., and McClain, A. C. (2023+). Sparse highdimensional linear regression of heteroscedastic data with a partitioned empirical Bayes ECM algorithm.
[Preprint]

Zgodic, A., Bai, R., Zhang, J., Wang, Y., and McLain, A. C. (2023+). Sparse highdimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm.
Deep Learning and Deep Generative Models

Wang, S., Shin, M., and Bai, R. (2023+). Generative quantile regression with variability penalty.
[Preprint] [Software]

Wang, S., Chakraborty, S., Qin, Q., and Bai, R. (2023+). A comprehensive deep generative framework for mixing density estimation.
Varying Coefficient Models
 Bai, R., Boland, M. R., and Chen, Y. (2023+). Scalable highdimensional Bayesian varying coefficient models with unknown withinsubject covariance.
[Preprint] [Software]

Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E., and Weiss, J. (2023+). VCBART: Bayesian trees for varying coefficients.
[Preprint] [Software]

Bai, R. (2023+). Adaptive posterior contraction for highdimensional Bayesian varying coefficient models under shrinkage priors.
Other Topics in Bayesian Modeling

Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2023+). A robust Bayesian Copas selection model for quantifying and correcting publication bias.
[Preprint] [Software]

Liu, Q., Huang, X., and Bai, R. (2023+). Bayesian modal regression based on mixture distributions.
[Preprint] [Software]

Bai, R.^{*}, Liu, X.^{*}, Lin, L., Liu, Y., Kimmel, S. E., Chu, H., and Chen, Y. (2023+). A Bayesian selection model for correcting outcome reporting bias with application to a metaanalysis on heart failure interventions.
[Preprint] [Software]
Spatiotemporal Modeling and Neighborhood Analysis

Meeker, J. R., Burris, H. H., Bai, R., Levine, L. D., and Boland, M. R. (2022). Neighborhood deprivation increases the risk of postinduction cesarean delivery. Journal of the American Medical Informatics Association, 29: 329334.
[Paper]

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, 2021: 545554.
[Paper]
 Duerr, I., Merrill, H. R., Wang, C., Bai, R., Boyer, M., 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. (2023+). Uncovering patterns for adverse pregnancy outcomes with a Bayesian spatial model: Evidence from Philadelphia.
[Preprint] [Software]