Valen Johnson

My current methodological research interests focus on problems related to Bayesian hypothesis testing, the use of Bayes factor functions to accurately report the outcomes of hypothesis tests using classical test statistics, applying Bayesian variable selection in ultra-high dimensional spaces, and latent variable models for ordinal and rank data analyses. In hypothesis testing and variable selection, I am particularly interested in exploring efficiencies that can be gained by using non-local prior densities to specify either alternative hypotheses in hypothesis testing problems or the non-null distributions of regression coefficients in variable selection problems. My research on ordinal and rank data modeling finds application in evaluating the intelligence of non-human primate species and educational assessment.