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Published/In Press
( _____ = student author, * = cofirst author)
2023

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]
2022

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]

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]
2021

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]

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]
2019

Bai, R. and Ghosh, M. (2019). Largescale multiple hypothesis testing with the normalbeta prime prior. Statistics, 53(6): 12101233.
[Paper] [Supplement] [Software]
2018

Bai, R. and Ghosh, M. (2018). Highdimensional multivariate posterior consistency under globallocal shrinkage priors. Journal of Multivariate Analysis, 167: 157170.
[Paper] [Supplement  Corrigendum] [Software]

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]
Preprints
( _____ = student author, * = cofirst author)

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

Wang, S., Shin, M., and Bai, R. (2024+). Generative quantile regression with variability penalty.
[Preprint] [Software]

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

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

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

Zgodic, A., Bai, R., Zhang, J., Wang, Y., Rorden, C., and McClain, A. C. (2024+). Sparse highdimensional linear regression of heteroscedastic data with a partitioned empirical Bayes ECM algorithm.
[Preprint]

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

Wang, S., Chakraborty, S., Qin, Q., and Bai, R. (2024+). A comprehensive deep generative framework for mixing density estimation.

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

Wang, S., Shin, M., and Bai, R. (2024+). Fast boostrapping nonparametric maximum likelihood for latent mixture models.

Zhao, Z., Li, Y., Luo, X., and Bai, R. (2024+). A threestate model framework to unify analytic methods for treatment crossover in oncology trials.

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