My primary research area is in Bayesian model selection. Specifically, I investigate the case where the levels of a categorical predictor fall into latent groups. I have developed a fully Bayesian model selection approach of clustering the data according to the levels of a categorical predictor to reveal latent group-based fixed effects, heteroscedasticity, and/or hidden interactions. Through the use of mixture g-priors and fractional Bayes factors, I test for both the presence and structure of such clustering. This method is broadly applicable to the class of linear models that include categorical predictors. Currently, I am working on developing an R package for this implementation and investigating Bayesian model averaging in this context. 

 

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Thomas Metzger joined the statistics faculty in 2019. He earned bachelor's degrees in mathematics and actuarial science, as well as a master's degree in secondary mathematics education, from The Ohio State University in 2010-11.