A class of multi-resolution approximations for large spatial datasets
Methodology
2020-12-22 v2 Computation
Abstract
Gaussian processes are popular and flexible models for spatial, temporal, and functional data, but they are computationally infeasible for large datasets. We discuss Gaussian-process approximations that use basis functions at multiple resolutions to achieve fast inference and that can (approximately) represent any spatial covariance structure. We consider two special cases of this multi-resolution-approximation framework, a taper version and a domain-partitioning (block) version. We describe theoretical properties and inference procedures, and study the computational complexity of the methods. Numerical comparisons and an application to satellite data are also provided.
Cite
@article{arxiv.1710.08976,
title = {A class of multi-resolution approximations for large spatial datasets},
author = {Matthias Katzfuss and Wenlong Gong},
journal= {arXiv preprint arXiv:1710.08976},
year = {2020}
}