Statistical Science

Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict

Noel Cressie, Sandy Burden, Walter Davis, Pavel N. Krivitsky, Payam Mokhtarian, Thomas Suesse, and Andrew Zammit-Mangion

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Abstract

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.

Article information

Source
Statist. Sci., Volume 30, Number 2 (2015), 170-175.

Dates
First available in Project Euclid: 3 June 2015

Permanent link to this document
https://projecteuclid.org/euclid.ss/1433341474

Digital Object Identifier
doi:10.1214/15-STS517

Mathematical Reviews number (MathSciNet)
MR3353099

Zentralblatt MATH identifier
1332.86009

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

Citation

Cressie, Noel; Burden, Sandy; Davis, Walter; Krivitsky, Pavel N.; Mokhtarian, Payam; Suesse, Thomas; Zammit-Mangion, Andrew. Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict. Statist. Sci. 30 (2015), no. 2, 170--175. doi:10.1214/15-STS517. https://projecteuclid.org/euclid.ss/1433341474


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See also

  • Main article: Cross-Covariance Functions for Multivariate Geostatistics.