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Parameter Estimation for Multivariate Spatial Lattice Models

As more spatial fields are being collected and analyzed in a wide variety of environmental problems, there is considerable effort in developing methodology for multivariate spatial models. One such model is the canonical multivariate conditional autoregressive (CAMCOR) model, which is a multivariate Markov random field model ideal for analyzing data on spatial grids or lattices. In this paper, an iterative approach to finding maximum likelihood estimates for the CAMCOR model parameters is discussed. The procedure is demonstrated on a case study focused on predicting crop yields on the basis of remote sensing data.