Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

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

The last decades have seen an unprecedented increase in the availability of data sets that
are inherently global and temporally evolving, from remotely sensed networks to climate …

Gaussian processes are arguably the most important model class in spatial statistics. They
encode prior information about the modeled function and can be used for exact or …

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 …

Despite its importance for insurance, there is almost no literature on statistical hail damage
modeling. Statistical models for hailstorms exist, though they are generally not open-source …

Large or very large spatial (and spatio-temporal) datasets have become common place in
many environmental and climate studies. These data are often collected in non-Euclidean …

The modeling of real-valued random fields indexed by spherical coordinates arises in
different disciplines of the natural sciences, especially in environmental, atmospheric and …

Highest density regions (HDRs) are defined as level sets containing sample points of
relatively high density. Although Euclidean HDR estimation from a random sample …

Reducing storage of global wind ensembles with stochastic generators Page 1 The Annals of
Applied Statistics 2018, Vol. 12, No. 1, 490–509 https://doi.org/10.1214/17-AOAS1105 © Institute …