Peer-reviewed Papers 

Porter, E.M., Franck, C.T. & Ferreira, M.A.R. (2023) Objective Bayesian model selection for spatial hierarchical models with intrinsic conditional autoregressive priors. Bayesian Analysis. Advance Publication 1 - 27. https://doi.org/10.1214/23-BA1375

Xu, S., Ferreira, M.A.R., Porter, E.M., & Franck, C.T. (2023) Bayesian model selection for generalized linear mixed models. Biometrics, 79, 3266– 3278. https://doi.org/10.1111/biom.13896

Ferreira, M.A.R., Porter, E.M., & Franck, C.T. (2021) Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects. Computational Statistics and Data Analysis, 162, 107264. https://doi.org/10.1016/j.csda.2021.107264

Porter, E.M., Franck, C.T. & Adams, S. Flexible cost-penalized Bayesian model selection: developing inclusion paths with an application to diagnosis of heart disease. Accepted for publication at Statistics in Medicine. arXiv:2305.06262

Porter, E.M., McMahan, C.S., Tebbs, J.M., & Bilder, C.R. Gradient boosting for group testing data.  In preparation.

Other Publications

Porter, E.M., Franck, C.T., & Ferreira, M.A.R. (202X), Gaussian Spatial Hierarchical Models with Intrinsic Conditional Autoregressive Priors, in Modeling Spatio-Temporal Data: Markov Random Fields, Objective Bayes, and Multiscale Models (Editor: Ferreira, M.A.R.), Boca Raton: Chapman Hall/CRC.