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dc.contributor.authorMa, Chunsheng
dc.date.accessioned2013-07-25T16:59:35Z
dc.date.available2013-07-25T16:59:35Z
dc.date.issued2007-03
dc.identifier.citationMa, Chunsheng. 2007. Why is isotropy so prevalent in spatial statistics?. Proceedings of the American Mathematical Society, v.135 no.3: pp.865-871en_US
dc.identifier.issn0002-9939
dc.identifier.issn1088-6826
dc.identifier.urihttp://dx.doi.org/10.1090/S0002-9939-06-08592-3
dc.identifier.urihttp://hdl.handle.net/10057/6053
dc.descriptionClick on the DOI link to access the article for free at the publisher's website.en_US
dc.description.abstractThere are many reasons for the popular use of the isotropic or geometrically anisotropic covariance function and variogram in spatial statistics. A less known reason demonstrated in this paper is that an isotropic or geometrically anisotropic model would be the only choice in certain circumstances, for instance, when the underlying random field is smooth enough.en_US
dc.language.isoen_USen_US
dc.publisherAmerican Mathematical Societyen_US
dc.relation.ispartofseriesProceedings of the American Mathematical Society;v.135 no.3
dc.subjectCovarianceen_US
dc.subjectGeometrically anisotropicen_US
dc.subjectIsotropicen_US
dc.subjectNegative definiteen_US
dc.subjectPositive definiteen_US
dc.subjectVariogramen_US
dc.subjectNorm (Mathematics)en_US
dc.titleWhy is isotropy so prevalent in spatial statistics?en_US
dc.typeArticleen_US
dc.description.versionPeer reviewed
dc.rights.holder© Copyright 2006 American Mathematical Society The copyright for this article reverts to public domain 28 years after publication.


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