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Semiparametric/Nonparametric Estimation and Variable Selection
Bai, R.*, Moran, G. E.*, Antonelli, J. L.*, Chen, Y., and Boland, M. R. (2020+). Spikeandslab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association (in press). (* = cofirst author)
[Paper] [Supplement] [R package]

Bai, R. (2020+). A unified computational and theoretical framework for highdimensional Bayesian additive models. Under review at Statistica Sinica.
[Preprint]

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 highdimensional 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]
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).
[Paper] [Supplement] [R package]

Bai, R. and Ghosh, M. (2019). Largescale multiple hypothesis testing with the normalbeta prime prior. Statistics, 53: 12101233.
[Paper] [Supplement] [R package]

Bai, R. and Ghosh, M. (2018). Highdimensional multivariate posterior consistency under globallocal shrinkage priors. Journal of Multivariate Analysis, 167: 157170.
[Paper] [Supplement] [R package]

Bai, R., Ročková, V., and George, E. I. (2020+). Spikeandslab meets lasso: A review of the spikeandslab lasso. In preparation.
MetaAnalysis

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 spacetime data. Environmental Modeling and Software, 102: 2938.
[link]

Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mowery, D., and Herman, D. (2020+). A method to link neighborhoodlevel covariates to COVID19 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. (* = cofirst 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)