title: A multivariate CAR model for mismatched lattices.
abstract: In this paper, we develop a multivariate Gaussian conditional autoregressive model for use on mismatched lattices. Most current multivariate CAR models are designed for each multivariate outcome to utilize the same lattice structure. In many applications, a change of basis will allow different lattices to be utilized, but this is not always the case, because a change of basis is not always desirable or even possible. Our multivariate CAR model allows each outcome to have a different neighborhood structure which can utilize different lattices for each structure. The model is applied in two real data analysis. The first is a Bayesian learning example in mapping the 2006 Iowa Mumps epidemic, which demonstrates the importance of utilizing multiple channels of infection flow in mapping infectious diseases. The second is a multivariate analysis of poverty levels and educational attainment in the American Community Survey.
authors.affiliation: Colorado School of Mines, Department of Applied Mathematics and Statistics, United States. Electronic address: aporter@mines.edu.
authors.forename: Aaron T
authors.initials: AT
authors.lastname: Porter
authors.affiliation: University of Iowa, Department of Biostatistics, United States. Electronic address: jacob-oleson@uiowa.edu.
authors.forename: Jacob J
authors.initials: JJ
authors.lastname: Oleson
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D058872:Epidemics / Q000706:statistics & numerical data*; D006801:Humans; D007484:Iowa; D008390:Markov Chains; D015233:Models, Statistical; D015999:Multivariate Analysis; D009107:Mumps / Q000453:epidemiology*; D016011:Normal Distribution; D011203:Poverty / Q000706:statistics & numerical data*; D014481:United States
keywords: American Community Survey; Conditional autoregressive; Infectious disease; Mismatched lattices