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Author Bachoc, F.; Porcu, E.; Bevilacqua, M.; Furrer, R.; Faouzi, T.
Title Asymptotically equivalent prediction in multivariate geostatistics Type
Year 2022 Publication Bernoulli Abbreviated Journal Bernoulli
Volume 28 Issue 4 Pages 2518-2545
Keywords Cokriging; equivalence of Gaussian measures; fixed domain asymptotics; functional analysis; Generalized Wendland; Matern; spectral analysis
Abstract Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geo-statistics. While best linear prediction has been well understood in univariate spatial statistics, the literature for the multivariate case has been elusive so far. The new challenges provided by modern spatial datasets, being typ-ically multivariate, call for a deeper study of cokriging. In particular, we deal with the problem of misspecified cokriging prediction within the framework of fixed domain asymptotics. Specifically, we provide conditions for equivalence of measures associated with multivariate Gaussian random fields, with index set in a compact set of a d-dimensional Euclidean space. Such conditions have been elusive for over about 50 years of spatial statistics. We then focus on the multivariate Matern and Generalized Wendland classes of matrix valued covariance functions, that have been very popular for having parameters that are crucial to spatial interpolation, and that control the mean square differentiability of the associated Gaussian process. We provide sufficient conditions, for equivalence of Gaussian measures, relying on the covariance parameters of these two classes. This enables to identify the parameters that are crucial to asymptotically equivalent interpolation in multivariate geostatistics. Our findings are then illustrated through simulation studies.
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Language Summary Language Original Title
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Series Volume Series Issue Edition
ISSN 1350-7265 ISBN Medium
Area Expedition Conference
Notes WOS:000843190100015 Approved (up)
Call Number UAI @ alexi.delcanto @ Serial 1639
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