Topics: High-dimensional statistics | Deep learning and deep generative models | Varying coefficient models
Survival analysis | Other topics in Bayesian modeling | Spatiotemporal modeling and neighborhood analysis


( _____ = student author, * = co-first author)

High-Dimensional statistics

  • 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, 117(537): 184-197.
    [Paper] [Supplement] [Software]

  • Bai, R. and Ghosh, M. (2021). On the beta prime prior for scale parameters in high-dimensional Bayesian regression models. Statistica Sinica, 31(2): 843-865.
    [Paper] [Supplement] [Software]

  • 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. (2019). Large-scale multiple hypothesis testing with the normal-beta prime prior. Statistics, 53(6): 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 || Corrigendum] [Software]

  • Bai, R. (2024+). Bayesian group regularization in generalized linear models with a continuous spike-and-slab prior.
    [Preprint] [Software]

  • Wang, S.-H., Bai, R., and Huang, H. H. (2024+). Two-step mixed-type 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+). Sparse high-dimensional 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. (2024+). Sparse high-dimensional 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.
    [Preprint] [Software]

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

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

Varying coefficient models

  • Bai, R., Boland, M. R., and Chen, Y. (2023). Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance. Journal of Machine Learning Research, 24(259): 1-49.
    [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]

  • Bai, R. (2024+). Adaptive posterior contraction for high-dimensional Bayesian varying coefficient models under shrinkage priors.

Survival analysis

  • Zhao, Z., Li, Y., Luo, X., and Bai, R. (2024+). A three-state 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.

Other topics in Bayesian modeling

  • 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]

  • Liu, Q., Huang, X., and Bai, R. (2024+). 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. (2024+). 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 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(2): 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(5): 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., 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. (2024+). Uncovering patterns for adverse pregnancy outcomes with a Bayesian spatial model: Evidence from Philadelphia.
    [Preprint] [Software]

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