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dc.contributor.authorMa, Chunshengen_US
dc.identifier.citationChunsheng Ma; , "Covariance Matrices for Second-Order Vector Random Fields in Space and Time," Signal Processing, IEEE Transactions on , vol.59, no.5, pp.2160-2168, May 2011 doi: 10.1109/TSP.2011.2112651en_US
dc.descriptionThe full text of this article is not available on SOAR. WSU users can access the article via IEEE Xplore database licensed by University Libraries:
dc.description.abstractThis paper deals with vector (or multivariate) random fields in space and/or time with second-order moments, for which a framework is needed for specifying not only the properties of each component but also the possible cross relationships among the components. We derive basic properties of the covariance matrix function of the vector random field and propose three approaches to construct covariance matrix functions for Gaussian or non-Gaussian random fields. The first approach is to take derivatives of a univariate covariance function, the second one is to work on the univariate random field whose index domain is in a higher dimension and the third one is based on the scale mixture of separable spatio-temporal covariance matrix functions. To illustrate these methods, many parametric or semiparametric examples are formulated.en_US
dc.relation.ispartofseriesSignal Processing, IEEE Transactions on , vol.59, no.5, pp.2160-2168en_US
dc.subjectAtmospheric measurementsen_US
dc.subjectAtmospheric modelingen_US
dc.subjectCovariance matrixen_US
dc.subjectSymmetric matricesen_US
dc.subjectTime series analysisen_US
dc.titleCovariance matrices for second-order vector random fields in space and timeen_US
dc.description.versionPeer reviewed articleen_US
dc.rights.holder© IEEE,2011en_US

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