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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(537): 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(2): 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(6): 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]

Bai, R. (2024+). Bayesian group regularization in generalized linear models with a continuous spikeandslab prior.
[Preprint] [Software]

Wang, S.H., Bai, R., and Huang, H. H. (2024+). Twostep mixedtype multivariate Bayesian sparse variable selection with shrinkage priors.
[Preprint] [Software]

Zgodic, A., Bai, R., Zhang, J., Wang, Y., Rorden, C., and McClain, A. C. (2024+). Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian highdimensional regression.
[Preprint]

Zgodic, A., Bai, R., Zhang, J., Wang, Y., and McLain, A. C. (2024+). Sparse highdimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm.
[Preprint] [Software]
Deep learning and deep generative models

Wang, S., Shin, M., and Bai, R. (2024). Generative quantile regression with variability penalty. Journal of Computational and Graphical Statistics (in press).
[Paper] [Supplement] [Software]

Wang, S., Shin, M., and Bai, R. (2024). Fast bootstrapping nonparametric maximum likelihood for latent mixture models. IEEE Signal Processing Letters (conditionally accepted).
[Paper] [Supplement] [Software]

Wang, S., Chakraborty, S., Qin, Q., and Bai, R. (2024+). A comprehensive deep generative framework for mixing density estimation.
Temporal and spatial models
 Bai, R., Boland, M. R., and Chen, Y. (2023). Scalable highdimensional Bayesian varying coefficient models with unknown withinsubject covariance. Journal of Machine Learning Research, 24(259): 149.
[Paper] [Software]

Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E., and Weiss, J. (2024+). VCBART: Bayesian trees for varying coefficients.
[Preprint] [Software]

Balocchi, C.^{*}, Bai, R.^{*}, Liu, J., Canelón, S. P., George, E. I., Chen, Y., and Boland, M. R. (2024+). Uncovering patterns for adverse pregnancy outcomes with a Bayesian spatial model: Evidence from Philadelphia.
[Preprint] [Software]

Bai, R. (2024+). Adaptive posterior contraction for highdimensional Bayesian varying coefficient models under shrinkage priors.
Survival analysis

Zhao, Z., Li, Y., Luo, X., and Bai, R. (2024+). A unified threestate model framework for analysis of treatment crossover in survival trials.
[Preprint] [Software]

Zhao, Z., Srivastava, S., Bandyopadhyay, D., and Bai, R. (2024+). Semiparametric Bayesian joint analysis of cluster size and survival time for kidney transplantation.
Other topics in Bayesian modeling

Liu, Q., Huang, X., and Bai, R. (2024+). Bayesian modal regression based on mixture distributions.
[Preprint] [Software]

Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2024+). A robust Bayesian Copas selection model for quantifying and correcting publication bias.
[Preprint] [Software]

Bai, R.^{*}, Liu, X.^{*}, Lin, L., Liu, Y., Kimmel, S. E., Chu, H., and Chen, Y. (2024+). A Bayesian selection model for correcting outcome reporting bias with application to a metaanalysis on heart failure interventions.
[Preprint] [Software]
Applied collaborations

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(2): 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(5): 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: A comparative study. Environmental Modeling and Software, 102: 2938.
[Paper]