Dear all,

I would like to announce the following talk that will be held on Thursday November 7th 2019, at 5 PM, in Aula D’Antoni of the Department of Mathematics, University of Rome Tor Vergata.

Speaker: Emilio Porcu (Trinity College Dublin)

Title: Modeling Temporally Evolving and Spatially Globally Dependent Data

Abstract: 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 model ensembles. This

paper provides an overview of statistical modeling techniques for

space–time processes, where space is the sphere representing our

planet. In particular, we make a distinction between (a) second

order‐based approaches and (b) practical approaches to modeling

temporally evolving global processes. The former approaches are based on

the specification of a class of space–time covariance functions, with

space being the two‐dimensional sphere. The latter are based on

explicit description of the dynamics of the space–time process, that

is, by specifying its evolution as a function of its past history with

added spatially dependent noise.

We focus primarily on approach (a), for which the literature has been

sparse. We provide new models of space–time covariance functions for

random fields defined on spheres cross time. Practical approaches (b)

are also discussed, with special emphasis on models built directly on

the sphere, without projecting spherical coordinates onto the plane.

We present a case study focused on the analysis of air pollution from

the 2015 wildfires in Equatorial Asia, an event that was classified as

the year's worst environmental disaster. The paper finishes with a list

of the main theoretical and applied research problems in the area, where

we expect the statistical community to engage over the next decade.

Kind regards,

Anna Vidotto