The rational SPDE approach for Gaussian random fields with general smoothness

TL;DR

This paper introduces the rational SPDE approach, enabling modeling of Gaussian random fields with arbitrary smoothness parameters, overcoming previous limitations and facilitating inference in spatial statistics.

Contribution

The paper proposes a novel rational SPDE method that applies to any smoothness parameter beyond the previous restrictions, improving flexibility in spatial modeling.

Findings

Method achieves explicit convergence rates in mean-square sense.

Computational benefits are maintained as in the restricted case.

Enables likelihood-based inference for all model parameters.

Abstract

A popular approach for modeling and inference in spatial statistics is to represent Gaussian random fields as solutions to stochastic partial differential equations (SPDEs) of the form $L^{\beta}u = \mathcal{W}$, where $\mathcal{W}$ is Gaussian white noise, $L$ is a second-order differential operator, and $\beta>0$ is a parameter that determines the smoothness of $u$. However, this approach has been limited to the case $2\beta\in\mathbb{N}$, which excludes several important models and makes it necessary to keep $\beta$ fixed during inference. We propose a new method, the rational SPDE approach, which in spatial dimension $d\in\mathbb{N}$ is applicable for any $\beta>d/4$, and thus remedies the mentioned limitation. The presented scheme combines a finite element discretization with a rational approximation of the function $x^{-\beta}$ to approximate $u$. For the resulting approximation, an explicit rate of convergence to $u$ in mean-square sense is derived. Furthermore, we show that our method has the same computational benefits as in the restricted case $2\beta\in\mathbb{N}$. Several numerical experiments and a statistical application are used to illustrate the accuracy of the method, and to show that it facilitates likelihood-based inference for all model parameters including $\beta$.