Bayesian Estimation of Parameters in Regression Models with Spatially Autocorrelated Errors


In regression analysis, it is usually assumed that the error terms are independent, but in practice we occasionally deal with many cases such as spatial data that the error terms in regression models are correlated and their correlation structure is a function of the observation locations. This type of models, namely spatial regression, are used for surface determination in geology, archaeology, epidemiology and image processing. In this paper, the Bayesian approach is used for spatial regression analysis with first order spatially autocorrelated errors. Because of computation difficulties of posterior distribution, MCMC methods are used for estimation of the posterior parameters. Then the efficiency of introduced method is considered in a simulation study for different sample and lattice sizes