Non-Stationary Covariance Estimation using the Stochastic Score Approximation for Large Spatial Data

Abstract

We introduce computational methods that allow for effective estimation of a flexible, parametric non-stationary spatial model when the field size is too large to compute the multivariate normal likelihood directly. In this method, the field is defined as a weighted spatially varying linear combination of a globally stationary process and locally stationary processes. Often in such a model, the difficulty in its practical use is in the definition of the boundaries for the local processes, and therefore we describe one such selection procedure that generally captures complex non-stationary relationships. We generalize the use of stochastic approximation to the score equations for data on a partial grid in this non-stationary case and provide tools for evaluating the approximate score in $O(n\log n)$ operations and $O(n)$ storage. We perform various simulations to explore the effectiveness and speed of the proposed methods and conclude by making inference on the accumulation behavior of arsenic applied to a sand grain.