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 …

Continuously indexed datasets with multiple variables have become ubiquitous in the
geophysical, ecological, environmental and climate sciences, and pose substantial analysis …

High-fidelity characterization and effective monitoring of spatial and spatiotemporal
processes are crucial for high-performance quality control of many manufacturing processes …

A stationary spatial model is an idealization and we expect that the true dependence
structures of physical phenomena are spatially varying, but how should we handle this non …

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 © …

Geostatistical models have traditionally been stationary. However, physical knowledge
about underlying spatial processes often requires models with non-stationary dependence …

Multivariate geostatistical data have become omnipresent in the geosciences and pose
substantial analysis challenges. One of them is the grouping of data locations into spatially …

Statistical models used in geophysical, environmental, and climate science applications
must reflect the curvature of the spatial domain in global data. Over the past few decades …

In many environmental applications involving spatially‐referenced data, limitations on the
number and locations of observations motivate the need for practical and efficient models for …

Large scale rainfall models are needed for collective risk estimation in flood insurance,
infrastructure networks and water resource management applications. There is a lack of …