† = student advised by Dr. Bai
§ = co-first author
* = alphabetical order

Semiparametric/Nonparametric Estimation and Variable Selection

  • Bai, R.§, Moran, G. E.§, Antonelli, J. L.§, Chen, Y., and Boland, M. R. (2021). 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. (2021+). Spike-and-slab group lasso for consistent estimation and variable selection in non-Gaussian generalized additive models. Revision submitted.
    [Preprint] [Software]

  • Bai, R., Jeong, S., and Ročková, V. (2021+). Minimax rates and adaptive procedures for generalized nonparametric regression. In preparation.

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. Handbook of Bayesian Variable Selection (Tadesse, M. and Vannucci, M., eds), Chapman & Hall/CRC Press (in 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]

Varying Coefficient and Time Series Models

  • Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E., and Weiss, J. (2021+). VCBART: Bayesian trees for varying coefficients. Under review.
    [Preprint] [Software]

  • Bai, R., Boland, M. R., and Chen, Y. (2021+). Fast algorithms and theory for high-dimensional Bayesian varying coefficient models. Under review.
    [Preprint] [Software]

  • Bai, R. (2021+). Uncertainty quantification for Bayesian vector autoregressive models. In preparation.


  • Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2021+). A robust Bayesian Copas selection model for quantifying and correcting publication bias. Under revision.
    [Preprint] [Software]

  • Bai, R., Liu, X., Lin, L., Chu, H., and Chen, Y. (2021+). ABSORB: A Bayesian Selection model for correcting and quantifying Outcome Reporting Bias in multivariate meta-analysis. In preparation.

Spatiotemporal Modeling and Neighborhood-Level Analysis

  • 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.

  • Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mellis, I., Mowery, D. L., and Herman, D. (2021). A method to link neighborhood-level covariates to COVID-19 infection patterns in Philadelphia using spatial regression. AMIA 2021 Virtual Informatics Summit, 2021 Mar 24.

  • 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.

  • Meeker, J. R., Burris, H. H., Bai, R., Levine, L. D., and Boland, M. R. (2021+). Neighborhood deprivation increases the risk of post-induction cesarean delivery. Revision submitted.

  • Balocchi, C.§, Bai, R.§, Liu, J., Canelón, S. P., George, E. I., Chen, Y., and Boland, M. R. (2021+). A Bayesian hierarchical modeling framework for geospatial analysis of adverse pregnancy outcomes. Under revision.
    [Preprint] [Software]

Theoretical Analysis of Algorithms

  • Bai, R. and Qin, Q. (2021+). Analysis of MCMC algorithms for Gaussian process regression with automatic relevance determination kernels. In preparation.