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Matérn Cross-Covariance Functions for Multivariate Random Fields

Gneiting, Tilmann ; Kleiber, William ; Schlather, Martin

Journal of the American Statistical Association, 2010-09, Vol.105 (491), p.1167-1177 [Periódico revisado por pares]

Alexandria, VA: Taylor & Francis

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Título:
Matérn Cross-Covariance Functions for Multivariate Random Fields
Autor: Gneiting, Tilmann ; Kleiber, William ; Schlather, Martin
Assuntos: Applications ; Atmospheric pressure ; Bivariate analysis ; Climate models ; Co-kriging ; Comparative analysis ; Convolution ; Correlation ; Correlation analysis ; Correlation coefficients ; Covariance ; Cross-correlation ; Data analysis ; Error ; Exact sciences and technology ; Fields (mathematics) ; Gaussian spatial random field ; General topics ; Inference from stochastic processes; time series analysis ; Linear analysis ; Linear models ; Mathematical functions ; Mathematical independent variables ; Mathematics ; Matérn class ; Maximum likelihood ; Modeling ; Multivariate analysis ; Multivariate geostatistics ; North America ; Numerical prediction ; Numerical weather prediction ; Pacific Region ; Parametric models ; Parsimony ; Positive definite ; Pressure ; Probability and statistics ; Probability theory and stochastic processes ; Process parameters ; Regression analysis ; Sciences and techniques of general use ; Smoothness ; Spatial models ; Statistical data ; Statistical methods ; Statistics ; Stochastic models ; Stochastic processes ; Temperature ; Theory and Methods ; Variance ; Weather forecasting
É parte de: Journal of the American Statistical Association, 2010-09, Vol.105 (491), p.1167-1177
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
Descrição: We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each constituent component is a Matérn process. The model parameters are interpretable in terms of process variance, smoothness, correlation length, and colocated correlation coefficients, which can be positive or negative. Both the marginal and the cross-covariance functions are of the Matérn type. In a data example on error fields for numerical predictions of surface pressure and temperature over the North American Pacific Northwest, we compare the bivariate Matérn model to the traditional linear model of coregionalization.
Editor: Alexandria, VA: Taylor & Francis
Idioma: Inglês

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Matérn Cross-Covariance Functions for Multivariate Random Fields

Gneiting, Tilmann ; Kleiber, William ; Schlather, Martin

Alexandria, VA: Taylor & Francis

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