Semiparametric/Nonparametric Estimation and Variable Selection

  • Bai, R.*, Moran, G. E.*, Antonelli, J. L.*, Chen, Y., and Boland, M. R. (2020+). Spike-and-slab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association (in press). (* = co-first author)
    [Paper] [Supplement] [R package]

  • Bai, R. (2020+). A unified computational and theoretical framework for high-dimensional Bayesian additive models. Under review at Statistica Sinica.

  • Bai, R., Jeong, S., and Ročková, V. (2020+). Minimax rates and adaptive procedures for nonparametric regression in the overdispersed exponential family. In preparation.

Varying Coefficient Models

  • Bai, R., Boland, M. R., and Chen, Y. (2020+). Fast algorithms and theory for high-dimensional Bayesian varying coefficient models.
    [Preprint] [R package]

  • Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E., and Weiss, J. (2020+). Estimating the effects of socioeconomic position on cognitive trajectories with Bayesian treed varying coefficient models. Under review at Journal of the American Statistical Association.
    [Preprint] [R package]

High-Dimensional Statistical Inference

  • Bai, R. and Ghosh, M. (2019+). On the beta prime prior for scale parameters in high-dimensional Bayesian regression models. Statistica Sinica (in press).
    [Paper] [Supplement] [R package]

  • Bai, R. and Ghosh, M. (2019). Large-scale multiple hypothesis testing with the normal-beta prime prior. Statistics, 53: 1210-1233.
    [Paper] [Supplement] [R package]

  • 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] [R package]

  • Bai, R., Ročková, V., and George, E. I. (2020+). Spike-and-slab meets lasso: A review of the spike-and-slab lasso.  In preparation.


  • Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2020+). A robust Bayesian Copas selection model for quantifying and correcting publication bias.
    [Preprint] [R package]

Spatiotemporal Modeling

  • 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.

  • Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mowery, D., and Herman, D. (2020+). A method to link neighborhood-level covariates to COVID-19 infection patterns in Philadelphia using spatial regression. Under review at AMIA 2021 Virtual Informatics Summit.

  • Bai, R.*, Balocchi, C.*, Liu, J., Canelón, S., George, E. I., Chen, Y., and Boland, M. R. (2020+). A Bayesian hierarchical modeling framework for geospatial analysis of adverse pregnancy outcomes. In preparation. (* = co-first author)

Theoretical Analysis of Algorithms

  • Bai, R. and Qin, Q. (2020+). Analysis of MCMC algorithms for Gaussian process regression with automatic relevance determination kernels. In preparation. († = alphabetical ordering)