Bayesian analysis of regression models with spatially correlated errors and missing observations

Abstract

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. © 2002 Elsevier Science B.V. All rights reserved.