Victor De Oliveira Bayesian model selection in spatial lattice models
The University of Texas at San Antonio
This work describes a Bayesian approach for model selection in Gaussian conditional
autoregressive models and Gaussian simultaneous autoregressive models which are commonly
used to describe spatial lattice data.
The approach is aimed at situations when all competing models have the same mean
structure, but differ on some aspects of their covariance structures.
The proposed approach uses as selection criterion the posterior model probabilities
computed using some default priors for the model parameters. The proposed methodology
is illustrated using two real datasets, one dealing with phosphate concentrations on an
archaeological region and the other dealing with homicide rates in southern US counties.
This is joint work with J. Song.