Families of covariance functions for bivariate random fields on spheres

Bevilacqua, M. and Diggle, P.J. and Porcu, E. (2020) Families of covariance functions for bivariate random fields on spheres. Spatial Statistics, 40: 100448. ISSN 2211-6753

[thumbnail of 10.1016@j.spasta.2020.100448]
Text (10.1016@j.spasta.2020.100448)
10.1016_j.spasta.2020.100448.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.

Download (2MB)


This paper proposes a new class of covariance functions for bivariate random fields on spheres, having the same properties as the bivariate Matérn model proposed in Euclidean spaces. The new class depends on the geodesic distance on a sphere; it allows for indexing differentiability (in the mean square sense) and fractal dimensions of the components of any bivariate Gaussian random field having such covariance structure. We find parameter conditions ensuring positive definiteness. We discuss other possible models and illustrate our findings through a simulation study, where we explore the performance of maximum likelihood estimation method for the parameters of the new covariance function. A data illustration then follows, through a bivariate data set of temperatures and precipitations, observed over a large portion of the Earth, provided by the National Oceanic and Atmospheric Administration Earth System Research Laboratory.

Item Type:
Journal Article
Journal or Publication Title:
Spatial Statistics
Additional Information:
This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 40, 2020 DOI: 10.1016/j.spasta.2020.100448
Uncontrolled Keywords:
?? great-circle distancecross correlationf classmatérn classmean square differentiabilitycomputers in earth sciencesstatistics and probabilitymanagement, monitoring, policy and law ??
ID Code:
Deposited By:
Deposited On:
22 Jul 2020 15:10
Last Modified:
12 Dec 2023 01:47