Inverses of Matern covariances on grids
成果类型：
Article
署名作者：
Guinness, Joseph
署名单位：
Cornell University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asab017
发表日期：
2022
页码：
535541
关键词：
摘要：
We conduct a study of the aliased spectral densities of Matern covariance functions on a regular grid of points, elucidating the properties of a popular approximation based on stochastic partial differential equations. While other researchers have shown that this approximation can work well for the covariance function, we find that it assigns too much power at high frequencies and does not provide increasingly accurate approximations to the inverse as the grid spacing goes to zero, except in the one-dimensional exponential covariance case.