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Activity Number: 348 - ENVR Student Paper Award Winners
Type: Topic-Contributed
Date/Time: Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics and the Environment
Abstract #317144
Title: Multi-Scale Vecchia Approximation of Gaussian Processes
Author(s): Jingjie Zhang* and Matthias Katzfuss
Companies: Texas A&M University and Texas A&M University
Keywords: covariance approximation; large datasets; computational complexity; sparsity; spatial statistics
Abstract:

Gaussian processes (GPs) are popular models for functions, time series, and spatial fields, but direct application of GPs is computationally infeasible for large datasets. We propose a multi-scale Vecchia (MSV) approximation of GPs for modeling and analysis of multi-scale phenomena, which are ubiquitous in geophysical and other applications. In the MSV approach, increasingly large sets of variables capture increasingly small scales of spatial variation, to obtain an accurate approximation of the spatial dependence from very large to very fine scales. For a given set of observations, the MSV approach decomposes the data into different scales, which can be visualized to obtain insights into the underlying processes. We explore properties of the MSV approximation and propose an algorithm for automatic choice of the tuning parameters. We provide comparisons to existing approaches based on simulated data and using satellite measurements of land-surface temperature.


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