期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 105, 期 491, 页码 1167-1177出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/jasa.2010.tm09420
关键词
Co-kriging; Convolution; Cross-correlation; Gaussian spatial random field; Matern class; Maximum likelihood; Multivariate geostatistics; Numerical weather prediction; Positive definite
资金
- Alfried Krupp von Bohlen und Halbach Foundation
- National Science Foundation [ATM-0724721, DMS-0706745]
- VIGRE program
- University Corporation for Atmospheric Research (UCAR) [S06-47225]
- DFG [FOR 916]
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.
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