Open Access
May 2015 Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict
Noel Cressie, Sandy Burden, Walter Davis, Pavel N. Krivitsky, Payam Mokhtarian, Thomas Suesse, Andrew Zammit-Mangion
Statist. Sci. 30(2): 170-175 (May 2015). DOI: 10.1214/15-STS517


Physical processes rarely occur in isolation, rather they influence and interact with one another. Thus, there is great benefit in modeling potential dependence between both spatial locations and different processes. It is the interaction between these two dependencies that is the focus of Genton and Kleiber’s paper under discussion. We see the problem of ensuring that any multivariate spatial covariance matrix is nonnegative definite as important, but we also see it as a means to an end. That “end” is solving the scientific problem of predicting a multivariate field.


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Noel Cressie. Sandy Burden. Walter Davis. Pavel N. Krivitsky. Payam Mokhtarian. Thomas Suesse. Andrew Zammit-Mangion. "Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict." Statist. Sci. 30 (2) 170 - 175, May 2015.


Published: May 2015
First available in Project Euclid: 3 June 2015

zbMATH: 1332.86009
MathSciNet: MR3353099
Digital Object Identifier: 10.1214/15-STS517

Keywords: Asymmetric cross-covariance function , conditional approach , factor process , latent process , nonstationarity

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.30 • No. 2 • May 2015
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