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

Abstract Models for the analysis of multivariate spatial data are receiving increased attention
these days. In many applications it will be preferable to work with multivariate spatial …

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 …

Soil pollution data collection typically studies multivariate measurements at sampling
locations, eg, lead, zinc, copper or cadmium levels. With increased collection of such …

In recent years, interest has grown in modeling spatio-temporal data generated from
monitoring networks, satellite imaging, and climate models. Under Gaussianity, the …

We derive a class of matrix valued covariance functions where the direct and cross-
covariance functions are Matérn. The parameters of the Matérn class are allowed to vary …

Spatial process models for analyzing geostatistical data entail computations that become
prohibitive as the number of spatial locations becomes large. There is a burgeoning …

We consider best linear unbiased prediction for multivariable data. Minimizing mean-
squared-prediction errors leads to prediction equations involving either covariances or …

The problem of constructing valid parametric cross-covariance functions is challenging. We
propose a simple methodology, based on latent dimensions and existing covariance models …

This paper presents an overview of results for the geostatistical analysis of collocated
multivariate data sets, whose variables form a composition, where the components …