Summary Continuously indexed Gaussian fields (GFs) are the most important ingredient in
spatial statistical modelling and geostatistics. The specification through the covariance …

A fundamental decision to make during the analysis of geostatistical data is the modeling of
the spatial dependence structure as stationary or non-stationary. Although second-order …

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 …

Gaussian random fields (GRFs) play an important part in spatial modelling, but can be
computationally infeasible for general covariance structures. An efficient approach is to …

Spatial processes with nonstationary and anisotropic covariance structure are often used
when modeling, analyzing, and predicting complex environmental phenomena. Such …

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

In this article, we propose a novel approach to modeling nonstationary spatial fields. The
proposed method works by expanding the geographic plane over which these processes …

The need for new methods to deal with big data is a common theme in most scientific fields,
although its definition tends to vary with the context. Statistical ideas are an essential part of …

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 …

Engineering systems can often be represented considering models at multiple levels.
Different properties within each level are typically inhomogeneous in space and cross …