Central limit theorems for long range dependent spatial linear processes

TL;DR

This paper establishes central limit theorems for sums of observations from spatial linear processes on multi-dimensional lattices, considering various dependence structures and the impact of sampling region geometry on asymptotic behavior.

Contribution

It provides new CLTs for spatial processes with different dependence types, highlighting the role of region shape and edge effects in higher dimensions.

Findings

Asymptotic variances differ by dependence type.

Region geometry influences variance and convergence rates.

Edge effects are significant in multi-dimensional negative dependence.

Abstract

Central limit theorems are established for the sum, over a spatial region, of observations from a linear process on a $d$-dimensional lattice. This region need not be rectangular, but can be irregularly-shaped. Separate results are established for the cases of positive strong dependence, short range dependence, and negative dependence. We provide approximations to asymptotic variances that reveal differential rates of convergence under the three types of dependence. Further, in contrast to the one dimensional (i.e., the time series) case, it is shown that the form of the asymptotic variance in dimensions $d>1$ critically depends on the geometry of the sampling region under positive strong dependence and under negative dependence and that there can be non-trivial edge-effects under negative dependence for $d>1$. Precise conditions for the presence of edge effects are also given.