Faster maximum likelihood estimation of very large spatial autoregressive models: An extension of the Smirnov-Anselin result
Maximization of an auto-Gaussian log-likelihood function when spatial autocorrelation is present requires numerical evaluation of an n x n matrix determinant. Griffith and Sone proposed a solution to this problem. This article simplifies and then evaluates an alternative approximation that can also be used with massively large georeferenced data sets based upon a regular square tessellation; this makes it particularly relevant to remotely sensed image analysis. Estimation results reported for five data sets found in the literature confirm the utility of this newer approximation.
maximum likelihood auto-gaussian spatial autocorrelation georeferenced data spatial statistics jacobian normalizing constant