Characterization Theorems for Pseudo-Variograms

12/05/2021
by   Christopher Dörr, et al.
0

Pseudo-variograms appear naturally in the context of multivariate Brown-Resnick processes, and are a useful tool for analysis and prediction of multivariate random fields. We give a necessary and sufficient criterion for a matrix-valued function to be a pseudo-variogram, and further provide a Schoenberg-type result connecting pseudo-variograms and multivariate correlation functions. By means of these characterizations, we provide extensions of the popular univariate space-time covariance model of Gneiting to the multivariate case.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/06/2022

Covariance Models for Multivariate Random Fields resulting from Pseudo Cross-Variograms

So far, the pseudo cross-variogram is primarily used as a tool for the s...
research
02/22/2022

Multivariate Gaussian Random Fields over Generalized Product Spaces involving the Hypertorus

The paper deals with multivariate Gaussian random fields defined over ge...
research
08/15/2018

Characterization of multivariate distributions by means of univariate one

The aim of this paper is to show a possibility to identify multivariate ...
research
07/29/2020

Asymptotically Equivalent Prediction in Multivariate Geostatistics

Cokriging is the common method of spatial interpolation (best linear unb...
research
08/09/2020

Visualization of Covariance Structures for Multivariate Spatio-Temporal Random Fields

The prevalence of multivariate space-time data collected from monitoring...
research
11/17/2020

Sampling with censored data: a practical guide

In this review, we present a simple guide for researchers to obtain pseu...
research
05/10/2023

Pseudo-reversing and its application for multiscaling of manifold-valued data

The well-known Wiener's lemma is a valuable statement in harmonic analys...

Please sign up or login with your details

Forgot password? Click here to reset