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Matern Cross-Covariance Functions for Multivariate Random Fields
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DOI：10.1198/jasa.2010.tm09420.png)
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We introduce a flexible parametric family of matrix-valued covariance functions for multivariate spatial random fields, where each constituent component is a Matern 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 Matern 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 Matern model to the traditional linear model of coregionalization.
Keyword:
Co-kriging
Convolution
Cross-correlation
Gaussian spatial random field
Matern class
Maximum likelihood
Multivariate geostatistics
Numerical weather prediction
Positive definite
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