Spatial statistics is concerned with the analysis of data that have spatial locations associated
with them, and those locations are used to model statistical dependence between the data …

Sampling is a technique from which information about the entire population can be inferred.
In case of remote sensing (RS) and geographic information system (GIS), training and test …

Global change analyses are facilitated by the growing number of remote-sensing datasets
that have both broad spatial extent and repeated observations over decades. These …

We introduce a class of scalable Bayesian hierarchical models for the analysis of massive
geostatistical datasets. The underlying idea combines ideas on high-dimensional …

For multivariate spatial Gaussian process models, customary specifications of cross-
covariance functions do not exploit relational inter-variable graphs to ensure process-level …

In multivariate spatio-temporal analysis, we are faced with the formidable challenge of
specifying a valid spatio-temporal cross-covariance function, either directly or through the …

The analysis of correlation structures among multivariate spatially aggregated data has
become increasingly important and poses substantial challenges. This article concerns the …

Modeling and inferring spatial relationships and predicting missing values of environmental
data are some of the main tasks of geospatial statisticians. These routine tasks are …

Highlights•We give a spatially-varying local cokriging method for large on-farm
experimentation data.•The new method could recommend high-resolution site-specific …

We set up a general framework for modeling non-Gaussian multivariate stochastic
processes by transforming underlying multivariate Gaussian processes. This general …