Interests My research interests include Bayesian modeling, spatial and space-time statistics, categorical data, and applications to environmental and epidemiological data.
Considering Spatial Confounding: Joint work with Kori Kahn in the Department of Statistics at Iowa State University. ArXiv paper
Generalized Poisson-P Regression Estimation: Joint work with Easton Huch, currently a PhD student in Statistics at The University of Michigan.
Bayesian analyses for medical applications: Joint work with Greg Snow in the Department of Statistics at BYU, and two undergraduate students, Abby Kennedy and Emmelia Cieslewicz. This work consists of two projects. The first looks at providing tools to help experts wisely choose prior distributions for treatment means. The second is an approach to correctly measure uncertainty in quantile regression for non-normally distributed residuals.
Examining the spread of COVID-19 through a university community: Joint work with Chantel Sloan in the Health Science Department at BYU and Mike Goodrich in the Computer Science Department at BYU. Using an agent-based model (ABM), we examine impact of different SARS-CoV-2 mitigation strategies on health outcomes. Inputs for the ABM are found using a Bayesian meta-analysis approach.
Bayesian Spatial Probit Model for Categorical Data: Research building extensively on my dissertation work with Kate Calder at The Ohio State University. R package for binary spatial classification: spProbit (Berrett, C. and Calder, C. A. "Bayesian spatial binary classification," Spatial Statistics, 16, 72-102.)
Scalable Statistical Validation and Uncertainty Quantification for Large Spatio-Temporal Datasets: NSF grant with Matt Heaton and Shane Reese in the Statistics Department at BYU, Will Kleiber in the Applied Math Department at UC-Boulder, and Doug Nychka in the Department of Applied Mathematics and Statistics at Colorado School of Mines.