M. Kathryn Cowles, PhD, and Juan Cervantes, PhD Candidate

A common problem in epidemiology, environmental science, econometrics, and other disciplines is to assess the relationships between response variables and covariates when both are measured over regions in two-dimensional space.  Examples include predicting county-by-county disease incidence or election outcomes using demographic, environmental, and/or economic variables.  A popular approach to accounting for spatial correlation in Bayesian models for such data is to include in the linear predictor a random effect for each region and to place a conditional-autoregressive (CAR) prior on the random effects.   For such models, this talk will survey possible motivations, implications for estimation of fixed effects, and various approaches to mitigating these effects.

Kate Cowles