Spectral adjustment for spatial confounding
Adjusting for an unmeasured confounder is generally an intractable problem, but in the
spatial setting it may be possible under certain conditions. We derive necessary conditions …
spatial setting it may be possible under certain conditions. We derive necessary conditions …
Graphical Gaussian process models for highly multivariate spatial data
For multivariate spatial Gaussian process models, customary specifications of cross-
covariance functions do not exploit relational inter-variable graphs to ensure process-level …
covariance functions do not exploit relational inter-variable graphs to ensure process-level …
Coherence and cross-spectral density matrix analysis of random wind and wave in deep water
J He - Ocean Engineering, 2020 - Elsevier
From the collected records of simultaneously measured wind and wave data during FETCH
experiment, the sample and mean power spectral density and coherence functions of the …
experiment, the sample and mean power spectral density and coherence functions of the …
Multivariate Mat\'ern Models--A Spectral Approach
The classical Mat\'ern model has been a staple in spatial statistics. Novel data-rich
applications in environmental and physical sciences, however, call for new, flexible vector …
applications in environmental and physical sciences, however, call for new, flexible vector …
Flexible modeling of variable asymmetries in cross-covariance functions for multivariate random fields
The geostatistical analysis of multivariate spatial data for inference as well as joint
predictions (co-kriging) ordinarily relies on modeling of the marginal and cross-covariance …
predictions (co-kriging) ordinarily relies on modeling of the marginal and cross-covariance …
Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso
We propose a new modeling framework for highly multivariate spatial processes that
synthesizes ideas from recent multiscale and spectral approaches with graphical models …
synthesizes ideas from recent multiscale and spectral approaches with graphical models …
Semiparametric estimation of cross-covariance functions for multivariate random fields
The prevalence of spatially referenced multivariate data has impelled researchers to
develop procedures for joint modeling of multiple spatial processes. This ordinarily involves …
develop procedures for joint modeling of multiple spatial processes. This ordinarily involves …
Vecchia Approximations and Optimization for Multivariate Mat\'ern Models
Y Fahmy, J Guinness - arXiv preprint arXiv:2210.09376, 2022 - arxiv.org
We describe our implementation of the multivariate Mat\'ern model for multivariate spatial
datasets, using Vecchia's approximation and a Fisher scoring optimization algorithm. We …
datasets, using Vecchia's approximation and a Fisher scoring optimization algorithm. We …
Nonparametric spectral methods for multivariate spatial and spatial–temporal data
J Guinness - Journal of multivariate analysis, 2022 - Elsevier
We propose computationally efficient methods for estimating stationary multivariate spatial
and spatial–temporal spectra from incomplete gridded data. The methods are iterative and …
and spatial–temporal spectra from incomplete gridded data. The methods are iterative and …
Cross‐dimple in the cross‐covariance functions of bivariate isotropic random fields on spheres
A Alegría - Stat, 2020 - Wiley Online Library
Multivariate random fields allow to simultaneously model multiple spatially indexed
variables, playing a fundamental role in geophysical, environmental, and climate disciplines …
variables, playing a fundamental role in geophysical, environmental, and climate disciplines …