Bayesian analysis of regression models with spatially correlated errors and missing observations
A Bayesian approach is proposed for estimating regression models on rectangular grids in which errors are spatially correlated and missing observations are present in the response variable. An easy and efficient Markov chain Monte Carlo algorithm is fully described for posterior inference on parameters and prediction of missing observations. Analysis of a real marine remote-sensing data set is presented to illustrate the method.
markov chain monte carlo posterior inference gibbs sampling algorithm missing value spatial data