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 …

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 …

Estimation of stationary dependence structure parameters using only a single realisation of
the spatial process, typically leads to inaccurate estimates and poorly identified parameters …

Modeling of the spatial dependence structure of environmental processes is fundamental to
almost all statistical analyses of data that are sampled spatially. The classical geostatistical …

We have previously formulated a Bayesian approach to the Sampson and Guttorp model for
the nonstationary correlation function r (x, x′) of a Gaussian spatial process [Damian et al …

Geophysical and other natural processes often exhibit nonstationary covariances and this
feature is important for statistical models that attempt to emulate the physical process. A …

Spatial models are used in a variety of research areas, such as environmental sciences,
epidemiology, or physics. A common phenomenon in such spatial regression models is …

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

Standard geostatistical models assume stationarity and rely on a variogram model to
account for the spatial dependence in the observed data. In some instances, this assumption …

In spite of the interest in and appeal of convolution-based approaches for nonstationary
spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution …