A flexible class of non-separable cross-covariance functions for multivariate space-time data
| Autor: | Emilio Porcu, Marc Bourotte, Denis Allard |
|---|---|
| Přispěvatelé: | Biostatistique et Processus Spatiaux (BIOSP), Institut National de la Recherche Agronomique (INRA), Departamento de Matemática, Universidad Tecnica Federico Santa Maria [Valparaiso] (UTFSM), metaprogramme Adaptation of Agriculture and Forests to Climate Change (AAFCC) /Proyecto Fondecyt from Chilean Ministry of Education 1130647, Biostatistique et Processus Spatiaux (BioSP) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Spatio-temporal processes Multivariate statistics Gaussian géostatistique 0208 environmental biotechnology Matern covariance Separability 02 engineering and technology Management Monitoring Policy and Law processus spatiotemporel 01 natural sciences Composite likelihood fonction de covariance Methodology (stat.ME) 010104 statistics & probability symbols.namesake [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST] Applied mathematics Statistics::Methodology geostatistics 0101 mathematics Computers in Earth Sciences Statistics - Methodology Mathematics probabilité composite Random field Multivariate Gaussian processes Univariate Cauchy distribution Covariance 020801 environmental engineering Positive definiteness Spatio-temporal geostatistics symbols gaussian process Cross-covariance processus gaussien |
| Zdroj: | Spatial Statistics Spatial Statistics, Elsevier, 2016, 18, pp.125-146. ⟨10.1016/j.spasta.2016.02.004⟩ |
| ISSN: | 2211-6753 |
| DOI: | 10.1016/j.spasta.2016.02.004⟩ |
| Popis: | Multivariate space–time data are increasingly available in various scientific disciplines. When analyzing these data, one of the key issues is to describe the multivariate space–time dependences. Under the Gaussian framework, one needs to propose relevant models for multivariate space–time covariance functions, i.e. matrix-valued mappings with the additional requirement of non-negative definiteness. We propose a flexible parametric class of cross-covariance functions for multivariate space–time Gaussian random fields. Space–time components belong to the (univariate) Gneiting class of space–time covariance functions, with Matern or Cauchy covariance functions in the spatial margins. The smoothness and scale parameters can be different for each variable. We provide sufficient conditions for positive definiteness. A simulation study shows that the parameters of this model can be efficiently estimated using weighted pairwise likelihood, which belongs to the class of composite likelihood methods. We then illustrate the model on a French dataset of weather variables. |
| Databáze: | OpenAIRE |
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