Maximum likelihood estimation of a spatial model typically requires a sizeable computational capacity, even in relatively small samples, and becomes unfeasible in very large datasets. The unilateral approximation approach to spatial models estimation (suggested in Besag, 1974) provides a viable alternative to maximum likelihood estimation that reduces substantially computing time and the storage required. In this paper we extend the method, originally proposed for conditionally speciﬁed processes, to simultaneous and to general bilateral spatial processes. We prove the estimators’ consistency and studytheir ﬁnite-sample propertiesvia Monte Carlo simulations.
- unilateral processes