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

This work gives an overview of an innovative class of methods for the analysis of spatial and
of functional data observed over complicated two‐dimensional domains. This class is based …

The advent of data science has provided an increasing number of challenges with high data
complexity. This paper addresses the challenge of space-time data where the spatial …

In this work, we propose a new approach to model large, irregularly distributed spatio‐
temporal global data via a locally diffusive stochastic partial differential equation (SPDE) …

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 …

A spectral algorithm is proposed to simulate an isotropic Gaussian random field on a sphere
equipped with a geodesic metric. This algorithm supposes that the angular power spectrum …

Functional spatio-temporal data naturally arise in many environmental and climate
applications where data are collected in a three-dimensional space over time. The MATLAB …

With the advent of wide-spread global and continental-scale spatiotemporal datasets,
increased attention has been given to covariance functions on spheres over time. This paper …

In this paper, we investigate a class of spherical functional autoregressive processes, and
we discuss the estimation of the corresponding autoregressive kernels. In particular, we first …

This survey contains a selection of topics unified by the concept of positive semidefiniteness
(of matrices or kernels), reflecting natural constraints imposed on discrete data (graphs or …