An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach
Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …
spatial statistical modelling and geostatistics. The specification through the covariance …
30 Years of space–time covariance functions
In this article, we provide a comprehensive review of space–time covariance functions. As
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …
for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit …
A case study competition among methods for analyzing large spatial data
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the
“big data” era, however, has lead to the traditional Gaussian process being computationally …
“big data” era, however, has lead to the traditional Gaussian process being computationally …
Cross-covariance functions for multivariate geostatistics
Continuously indexed datasets with multiple variables have become ubiquitous in the
geophysical, ecological, environmental and climate sciences, and pose substantial analysis …
geophysical, ecological, environmental and climate sciences, and pose substantial analysis …
[图书][B] Random fields for spatial data modeling
DT Hristopulos - 2020 - Springer
The series aims to: present current and emerging innovations in GIScience; describe new
and robust GIScience methods for use in transdisciplinary problem solving and decision …
and robust GIScience methods for use in transdisciplinary problem solving and decision …
Efficient algorithms for Bayesian nearest neighbor Gaussian processes
We consider alternate formulations of recently proposed hierarchical nearest neighbor
Gaussian process (NNGP) models for improved convergence, faster computing time, and …
Gaussian process (NNGP) models for improved convergence, faster computing time, and …
Limitations on low rank approximations for covariance matrices of spatial data
ML Stein - Spatial Statistics, 2014 - Elsevier
Evaluating the likelihood function for Gaussian models when a spatial process is observed
irregularly is problematic for larger datasets due to constraints of memory and calculation. If …
irregularly is problematic for larger datasets due to constraints of memory and calculation. If …
The Matérn model: A journey through statistics, numerical analysis and machine learning
The Matern Model: A Journey Through Statistics, Numerical Analysis and Machine Learning
Page 1 Statistical Science 2024, Vol. 39, No. 3, 469–492 https://doi.org/10.1214/24-STS923 © …
Page 1 Statistical Science 2024, Vol. 39, No. 3, 469–492 https://doi.org/10.1214/24-STS923 © …
Reshaping geostatistical modeling and prediction for extreme-scale environmental applications
We extend the capability of space-time geostatistical modeling using algebraic
approximations, illustrating application-expected accuracy worthy of double precision from …
approximations, illustrating application-expected accuracy worthy of double precision from …
High-order composite likelihood inference for max-stable distributions and processes
In multivariate or spatial extremes, inference for max-stable processes observed at a large
collection of points is a very challenging problem and current approaches typically rely on …
collection of points is a very challenging problem and current approaches typically rely on …