Semiparametric/Nonparametric Estimation and Variable Selection
Bai, R.^{§}, Moran, G. E.^{§}, Antonelli, J. L.^{§}, Chen, Y., and Boland, M. R. (2022). Spike-and-slab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association (in press).
[Paper] [Supplement] [Software]
Bai, R. (2022+). Spike-and-slab group lasso for consistent estimation and variable selection in non-Gaussian generalized additive models.
[Preprint] [Software]
High-Dimensional Statistics
Bai, R., Ročková, V., and George, E. I. (2021). Spike-and-slab meets LASSO: A review of the spike-and-slab LASSO. In Tadesse, M. G. and Vannucci, M. (Eds.), Handbook of Bayesian Variable Selection, 81-108. Chapman & Hall/CRC Press.
[Paper] [Software]
Bai, R. and Ghosh, M. (2021). On the beta prime prior for scale parameters in high-dimensional Bayesian regression models. Statistica Sinica, 31: 843-865.
[Paper] [Supplement] [Software]
Bai, R. and Ghosh, M. (2019). Large-scale multiple hypothesis testing with the normal-beta prime prior. Statistics, 53: 1210-1233.
[Paper] [Supplement] [Software]
Bai, R. and Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167: 157-170.
[Paper] [Supplement] [Software]
Wang, S.-H., Bai, R., and Huang, H. H. (2022+). On the proof of posterior contraction for sparse generalized linear models with multivariate responses.
Longitudinal Data Analysis
Bai, R., Boland, M. R., and Chen, Y. (2021+). Fast algorithms and theory for high-dimensional Bayesian varying coefficient models. Under revision.
[Preprint] [Software]
Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E., and Weiss, J. (2022+). VCBART: Bayesian trees for varying coefficients.
[Preprint] [Software]
Meta-Analysis
Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2022+). A robust Bayesian Copas selection model for quantifying and correcting publication bias. Under revision.
[Preprint] [Software]
Bai, R.^{§}, Liu, X.^{§}, Lin, L., Liu, Y., Kimmel, S. E., Chu, H., and Chen, Y. (2022+). A Bayesian selection model for correcting outcome reporting bias with application to a meta-analysis on heart failure interventions.
[Preprint] [Software]
Spatiotemporal Modeling and Neighborhood-Level Analysis
Meeker, J. R., Burris, H. H., Bai, R., Levine, L. D., and Boland, M. R. (2022). Neighborhood deprivation increases the risk of post-induction cesarean delivery. Journal of the American Medical Informatics Association, 29: 329-334.
[Paper]
Meeker, J. R., Canelón, S. P., Bai, R., Levine, L. D., and Boland, M. R. (2021). Individual- and neighborhood-level risk factors for severe maternal morbidity. Obstetrics & Gynecology, 137: 847-854.
[Paper]
Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mellis, I., Mowery, D. L., and Herman, D. (2021). Association of neighborhood-level factors and COVID-19 infection patterns in Philadelphia using spatial regression. AMIA Annual Symposium Proceedings, 2021: 545-554.
[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 space-time data. Environmental Modeling and Software, 102: 29-38.
[Paper]
Balocchi, C.^{§}, Bai, R.^{§}, Liu, J., Canelón, S. P., George, E. I., Chen, Y., and Boland, M. R. (2022+). A Bayesian hierarchical modeling framework for geospatial analysis of adverse pregnancy outcomes. Under revision.
[Preprint] [Software]