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arXiv · 1710.08976

A class of multi-resolution approximations for large spatial datasets

Matthias Katzfuss·Wenlong Gong
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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.

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Matthias Katzfuss, Wenlong Gong. 2018-07-20. A class of multi-resolution approximations for large spatial datasets. https://doi.org/10.1007/s13253-020-00401-7

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