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( _____ = student author, * = co-first author)
Deep learning and deep generative models
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Wang, S., Shin, M., and Bai, R. (2024+). Generative quantile regression with variability penalty. Journal of Computational and Graphical Statistics (in press).
[Paper] [Supplement] [Software]
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Wang, S., Shin, M., and Bai, R. (2024). Fast bootstrapping nonparametric maximum likelihood for latent mixture models. IEEE Signal Processing Letters, 31: 870-874.
[Paper] [Supplement] [Software]
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Wang, S., Chakraborty, S., Qin, Q., and Bai, R. (2024+). Neural-g: A deep learning framework for mixing density estimation.
[Preprint] [Software]
High-dimensional statistics
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Wang, S.-H., Bai, R., and Huang, H. H. (2024+). Two-step mixed-type multivariate Bayesian sparse variable selection with shrinkage priors. Electronic Journal of Statistics (minor revision submitted).
[Paper] [Software]
- Bai, R.*, Moran, G. E.*, Antonelli, J. L.*, Chen, Y., and Boland, M. R. (2022). Spike-and-slab group lassos for grouped regression and sparse generalized additive models. Journal of the American Statistical Association, 117(537): 184-197.
[Paper] [Supplement] [Software]
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Bai, R. and Ghosh, M. (2021). On the beta prime prior for scale parameters in high-dimensional Bayesian regression models. Statistica Sinica, 31(2): 843-865.
[Paper] [Supplement] [Software]
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Bai, R., Ročková, V., and George, E. I. (2021). Spike-and-slab meets LASSO: A review of the spike-and-slab LASSO. In Tadesse, M. G. and Vannucci, M. (Eds.), Handbook of Bayesian Variable Selection, 81-108. Chapman & Hall/CRC Press.
[Paper] [Software]
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Bai, R. and Ghosh, M. (2019). Large-scale multiple hypothesis testing with the normal-beta prime prior. Statistics, 53(6): 1210-1233.
[Paper] [Supplement] [Software]
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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 || Corrigendum] [Software]
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Bai, R. (2024+). Bayesian group regularization in generalized linear models with a continuous spike-and-slab prior.
[Preprint] [Software]
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Zgodic, A., Bai, R., Zhang, J., Wang, Y., Rorden, C., and McClain, A. C. (2024+). Quantifying predictive uncertainty of aphasia severity in stroke patients with sparse heteroscedastic Bayesian high-dimensional regression.
[Preprint]
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Zgodic, A., Bai, R., Zhang, J., Wang, Y., Olejua, P., and McLain, A. C. (2024+). Sparse high-dimensional linear mixed modeling with a partitioned empirical Bayes ECM algorithm.
[Preprint] [Software]
Temporal and spatial models
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Deshpande, S. K., Bai, R., Balocchi, C., and Starling, J. E., and Weiss, J. (2024+). VCBART: Bayesian trees for varying coefficients. Bayesian Analysis (in press).
[Paper] [Supplement] [Software]
- Bai, R., Boland, M. R., and Chen, Y. (2023). Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance. Journal of Machine Learning Research, 24(259): 1-49.
[Paper] [Software]
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Balocchi, C.*, Bai, R.*, Liu, J., Canelón, S. P., George, E. I., Chen, Y., and Boland, M. R. (2024+). Quantifying patient and neighborhood risks for stillbirth and preterm birth in Philadelphia with a Bayesian spatial model.
[Preprint] [Software]
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Bai, R. (2024+). Adaptive posterior contraction for high-dimensional Bayesian varying coefficient models under shrinkage priors.
Matrix completion
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Fan, S., Xiong, L., Wang, D., Cai, G., and Bai, R. (2024+). Simultaneous drug discovery and clustering
with spike-and-slab lasso matrix completion.
Survival analysis
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Zhao, Z., Li, Y., Luo, X., and Bai, R. (2024+). A unified three-state model framework for analysis of treatment crossover in survival trials.
[Preprint] [Software]
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Zhao, Z., Srivastava, S., Bandyopadhyay, D., and Bai, R. (2024+). Semiparametric Bayesian joint analysis of cluster size and survival time for kidney transplantation.
Other topics in Bayesian modeling
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Liu, Q., Huang, X., and Bai, R. (2024). Bayesian modal regression based on mixture distributions. Computational Statistics & Data Analysis, 199: 108012.
[Paper] [Supplement] [Software]
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Bai, R., Lin, L., Boland, M. R., and Chen, Y. (2024+). A robust Bayesian Copas selection model for quantifying and correcting publication bias.
[Preprint] [Software]
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Bai, R.*, Liu, X.*, Lin, L., Liu, Y., Kimmel, S. E., Chu, H., and Chen, Y. (2024+). A Bayesian selection model for correcting outcome reporting bias with application to a meta-analysis on heart failure interventions.
[Preprint] [Software]
Applied collaborations
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Meeker, J. R., Burris, H. H., Bai, R., Levine, L. D., and Boland, M. R. (2022). Neighborhood deprivation increases the risk of post-induction cesarean delivery. Journal of the American Medical Informatics Association, 29(2): 329-334.
[Paper]
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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(5): 847-854.
[Paper]
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Boland, M. R., Liu, J., Balocchi, C., Meeker, J., Bai, R., Mellis, I., Mowery, D. L., and Herman, D. (2021). Association of neighborhood-level factors and COVID-19 infection patterns in Philadelphia using spatial regression. AMIA Annual Symposium Proceedings, 2021: 545-554.
[Paper]
- Duerr, I., Merrill, H. R., Wang, C., Bai, R., Boyer, M., Dukes, M. D., and Bliznyuk, N. (2018). Forecasting urban water demand with statistical and machine learning methods using large space-time data: A comparative study. Environmental Modeling and Software, 102: 29-38.
[Paper]