Nonparametric Estimation and Variable Selection
Bai, R.*, Moran, G. E.*, Antonelli, J. L.*, Chen, Y., and Boland, M. R. (2019+). Spikeandslab group lassos for grouped regression and sparse generalized additive models. In revision for Journal of the American Statistical Association. (* = cofirst author)
[preprint]
Varying Coefficient Models
Bai, R., Boland, M. R., and Chen, Y. (2019+). Fast algorithms and theory for highdimensional Bayesian varying coefficient models. In revision for Journal of the American Statistical Association.
[preprint]
Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E. (2019+). VCBART: Bayesian trees meet varying coefficients. In preparation.
HighDimensional Statistical Inference
Bai, R. and Ghosh, M. (2019). On the beta prime prior for scale parameters in highdimensional Bayesian regression models. Statistica Sinica, in press.
[pdf] [supplement]

Bai, R. and Ghosh, M. (2019). Largescale multiple hypothesis testing with the normalbeta prime prior. Statistics, in press.
[pdf] [supplement]

Bai, R. and Ghosh, M. (2018). Highdimensional multivariate posterior consistency under globallocal shrinkage priors. Journal of Multivariate Analysis, 167: 157170.
[pdf] [supplement]
Bai, R., Ročková, V., and George, E. I. (2019+). Spikeandslab meets lasso: A review of the spikeandslab lasso. In preparation.
MetaAnalysis

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

^{†} Bai, R. and Qin, Q. (2019+). Analysis of MCMC algorithms for Gaussian process regression with automatic relevance determination kernels. In preparation. († = alphabetical ordering)
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 spacetime data. Environmental Modeling and Software, 102: 2938.
[link]