fitting spatial regression to large datasets using unilateral approximations

Maximum likelihood estimation of spatial models typically requires a sizeable computational\r\ncapacity, even in relatively small samples and becomes unfeasible in very large datasets. The\r\nunilateral approximation approach to spatial models estimation (suggested in Besag, 1974) provides\r\na viable alternative to maximum likelihood estimation that reduces substantially computing\r\ntime and the storage required. Originally proposed for conditionally specified processes, in this 20\r\npaper we extend the method to simultaneous and to general bilateral spatial processes. We prove\r\nconsistency of the estimators and we study their finite-sample properties via Monte Carlo simulations.