A Fused Gaussian Process Model for Very Large Spatial Data

TL;DR

This paper introduces a fused Gaussian process model for analyzing very large spatial datasets, combining flexible and parametric covariance components to improve computational efficiency and robustness.

Contribution

It proposes a novel semiparametric fused Gaussian process model that effectively handles massive spatial data with improved inference and prediction capabilities.

Findings

Demonstrates computational efficiency over existing methods

Shows robustness against model misspecification

Captures spatial nonstationarity effectively

Abstract

With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. This article proposes a semiparametric approach to model large or massive spatial datasets. In particular, a Gaussian process with additive components is proposed, with its covariance structure consisting of two components: one component is flexible without assuming a specific parametric covariance function but is able to achieve dimension reduction; the other is parametric and simultaneously induces sparsity. The inference algorithm for parameter estimation and spatial prediction is devised. The resulting spatial prediction methodology that we call fused Gaussian process (FGP), is applied to simulated data and a massive satellite dataset. The results demonstrate the computational and inferential benefits of FGP over competing methods and show that FGP is robust against model misspecification and captures spatial nonstationarity. The supplemental materials are available online.