Bayesian nonstationary and nonparametric covariance estimation for large spatial data

TL;DR

This paper introduces a scalable Bayesian method for estimating nonstationary, nonparametric covariance structures in large spatial datasets, improving flexibility over traditional stationary models.

Contribution

It proposes a novel Bayesian approach that infers sparse Cholesky factors for the precision matrix, enabling efficient nonstationary covariance estimation in large spatial data.

Findings

Method is highly scalable and parallelizable.

Numerical comparisons show improved estimation accuracy.

Application to climate data demonstrates practical utility.

Abstract

In spatial statistics, it is often assumed that the spatial field of interest is stationary and its covariance has a simple parametric form, but these assumptions are not appropriate in many applications. Given replicate observations of a Gaussian spatial field, we propose nonstationary and nonparametric Bayesian inference on the spatial dependence. Instead of estimating the quadratic (in the number of spatial locations) entries of the covariance matrix, the idea is to infer a near-linear number of nonzero entries in a sparse Cholesky factor of the precision matrix. Our prior assumptions are motivated by recent results on the exponential decay of the entries of this Cholesky factor for Matern-type covariances under a specific ordering scheme. Our methods are highly scalable and parallelizable. We conduct numerical comparisons and apply our methodology to climate-model output, enabling statistical emulation of an expensive physical model.