Hui, F. K. C., Dang, K.-D., and Maestrini, L. (2024). Simultaneous Coefficient Clustering and Sparsity for Multivariate Mixed Models. Journal of Computational and Graphical Statistics, 34(2):618–629
Akkaya Hocagil, T., Ryan, L. M., Cook, R. J., Dang, K.-D., Carter, R. C., Richardson, G. A., Day, N. L., Coles, C. D., Carmichael Olson, H., Jacobson, S. W., and Jacobson, J. L. (2024). Benchmark dose profiles for bivariate exposures. Risk Analysis, 44(10):2415–2428
Jacobson, J. L., Akkaya Hocagil, T., Jacobson, S. W., Coles, C. D., Richardson, G. A., Carmichael Olson, H., Day, N. L., Carter, C. R., Dodge, N. C., Dang, K.-D., Cook, R. J., and Ryan, L. M. (2024) A dose–response analysis of the effects of prenatal alcohol exposure on cognitive development. Alcohol: Clinical and Experimental Research, 48, 623–639
Li, K., Akkaya Hocagil, T., Cook, R. J., Ryan, L. M., Carter, R. C., Dang, K.-D., Jacobson, J. L., and Jacobson, S. W. (2023). Use of generalized propensity scores for assessing effects of multiple exposures. Statistics in Biosciences
Dang, K.-D., Ryan, L. M., Akkaya-Hocagil, T., Cook, R. J., Richardson, G. A., Day, N. L., Coles, C. D., Olson, H. C., Jacobson, S. W., and Jacobson, J. L. (2023). Bayesian modelling of effects of prenatal alcohol exposure on child cognition based on data from multiple cohorts. Australian & New Zealand Journal of Statistics, 65(3):167–186
Dang, K.-D., Ryan, L. M., Cook, R. J., Akkaya Hocagil, T., Jacobson, S. W., & Jacobson, J. L. (2023). Bayesian outcome selection modeling. Stat, 12( 1), e568
Dang, K.-D. and Maestrini, L. (2022). Fitting structural equation models via variational approximations. Structural Equation Modeling: A Multidisciplinary Journal, 29(6):839–853
Quiroz, M., Tran, M.-N., Villani, M., Kohn, R., and Dang, K.-D. (2021). The block-Poisson estimator for optimally tuned exact subsampling MCMC. Journal of Computational and Graphical Statistics, 30(4):877–888
Gunawan, D., Dang, K.-D., Quiroz, M., Kohn, R., and Tran, M.-N. (2020). Subsampling sequential Monte Carlo for static Bayesian models. Statistics and Computing, 30(6):1741–1758
Dang, K.-D., Quiroz, M., Kohn, R., Tran, M.-N., and Villani, M. (2019). Hamiltonian Monte Carlo with energy conserving subsampling. Journal of Machine Learning Research, 20(100):1–31
Dang, D. K.-D., Patterson, A. C., and Carrasco, L. R. (2019). An analysis of the spatial association between deforestation and agricultural field sizes in the tropics and subtropics. PloS One, 14(1):e0209918
Quiroz, M., Villani, M., Kohn, R., Tran, M.-N., and Dang, K.-D. (2018). Subsampling MCMC - an introduction for the survey statistician. Sankhya A, 80:33–69
Dang, K. D., Maestrini, L., & Hui, F. K. (2024). Variational Bayes for Mixture of Gaussian Structural Equation Models. arXiv preprint arXiv:2407.08140.
Saikai, Y., & Dang, K.-D. (2023). Mixtures of Gaussian process experts based on kernel stick-breaking processes. arXiv preprint arXiv:2304.13833.