Papers Under Invited Revision

  • Bai, R., Boland, M. R., and Chen, Y. (2019+). Fast algorithms and theory for high-dimensional Bayesian varying coefficient models. In revision for Journal of the American Statistical Association.
    [preprint]

  • Bai, R.*, Moran, G. E.*, Antonelli, J. L.*, Chen, Y., and Boland, M. R. (2019+). Spike-and-slab group lassos for grouped regression and sparse generalized additive models. In revision for Journal of the American Statistical Association. (* = co-first author)
    [preprint]

2019

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

  • Bai, R. and Ghosh, M. (2019). Large-scale multiple hypothesis testing with the normal-beta prime prior. Statistics, in press.
    [pdf] [supplement]

2018

  • Bai, R. and Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167: 157-170.
    [pdf] [supplement]

  • 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.
    [link]

Workings Papers and Papers in Preparation

  • Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E. (2019+). VC-BART: Bayesian trees meet varying coefficients. In preparation.

  • Bai, R., Boland, M. R., and Chen, Y. (2019+). A robust Bayesian Copas selection model for detecting and correcting publication bias. In preparation.

  • Bai, R. and Qin, Q. (2019+). Analysis of MCMC algorithms for Gaussian process regression with automatic relevance determination kernels. In preparation. († = alphabetical ordering)

  • Bai, R., Ročková, V., and George, E. I. (2019+). Spike-and-slab meets lasso: A review of the spike-and-slab lasso.  In preparation.