Fully nonseparable Gneiting covariance functions for multivariate space-time data - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year : 2022

Fully nonseparable Gneiting covariance functions for multivariate space-time data

Abstract

We broaden the well-known Gneiting class of space-time covariance functions by introducing a very general parametric class of fully nonseparable direct and crosscovariance functions for multivariate random fields, where each component has a spatial covariance function from the Matérn family with its own smoothness and scale parameters and, unlike all currently available models, its own correlation function in time. We present sufficient conditions that result in valid models with varying degrees of complexity and we discuss the parameterization of those. Continuous-in-space and discrete-in-time simulation algorithms are also given, which are not limited by the number of target spatial coordinates and allow tens of thousands of time coordinates. The application of the proposed model is illustrated on a weather trivariate dataset over France. Our new model yields better fitting and better predictive scores compared to a more parsimonious model with common temporal correlation function.
Fichier principal
Vignette du fichier
MultiGM.pdf (809.4 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03564931 , version 1 (10-02-2022)

Identifiers

  • HAL Id : hal-03564931 , version 1

Cite

Denis Allard, Lucia Clarotto, Xavier Emery. Fully nonseparable Gneiting covariance functions for multivariate space-time data. 2022. ⟨hal-03564931⟩
161 View
233 Download

Share

Gmail Facebook X LinkedIn More