Approximate Likelihood for Large Irregularly Spaced Spatial Data
Approximate Likelihood for Large Irregularly Spaced Spatial Data
AbstractLikelihood approaches for large, irregularly spaced spatial datasets are often very difficult, if not infeasible, to implement due to computational limitations. Even when we can assume normality, exact calculations of the likelihood for a Gaussian spatial process observed at n locations requires O(n3) operations. We present a version of Whittle's …