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Spat Spatiotemporal Epidemiol. 2014 Oct;11:79-88. doi: 10.1016/j.sste.2014.08.001. Epub 2014 Sep 18.

A multivariate CAR model for mismatched lattices.

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Colorado School of Mines, Department of Applied Mathematics and Statistics, United States. Electronic address:
University of Iowa, Department of Biostatistics, United States. Electronic address:


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.


American Community Survey; Conditional autoregressive; Infectious disease; Mismatched lattices

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