Multivariate spatial field data are increasingly common and whose modeling typically relies
on building cross-covariance functions to describe cross-process relationships. An …

Multivariate spatial field data are increasingly common and their modeling typically relies on
building cross-covariance functions to describe cross-process relationships. An alternative …

It is well-known that the crucial ingredient for a vector Gaussian random function is its
covariance matrix, where a diagonal entry termed a direct covariance is simply the …

The prevalence of multivariate space-time data collected from monitoring networks and
satellites or generated from numerical models has brought much attention to multivariate …

Analyzing time series in the frequency domain enables the development of powerful tools for
investigating the second-order characteristics of multivariate processes. Parameters like the …

Spatial data with several components, such as observations of air temperature and pressure
in a certain geographical region or the content of two metals in a geological deposit, require …

The modeling of spatio-temporal and multivariate spatial random fields has been an
important and growing area of research due to the increasing availability of spacetime …

A random field is the representation of the joint probability distribution for a set of random
variables. Markov fields, in particular, have a long standing tradition as the theoretical …

The question of how best to estimate a continuous probability density from finite data is an
intriguing open problem at the interface of statistics and physics. Previous work has argued …

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