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( _____ = student author, * = cofirst author)
Deep Learning and Deep Generative Models

Wang, S., Shin, M., and Bai, R. (2023+). Generative quantile regression with variability penalty.
[Preprint] [Software]
Semiparametric/Nonparametric Estimation and Variable Selection
 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. (2023+). Spikeandslab group lasso for consistent estimation and variable selection in nonGaussian generalized additive models.
[Preprint] [Software]
 Bai, R., Boland, M. R., and Chen, Y. (2023+). NVCSSL: Nonparametric varying coefficient spikeandslab lasso for highdimensional Bayesian varying coefficient models
[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]
HighDimensional Statistics

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+). Mixedtype multivariate Bayesian sparse variable selection with shrinkage priors.
[Preprint] [Software]
Modal Regression Models

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

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]

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 spatial analysis: Evidence from Philadelphia.
[Preprint] [Software]