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Capturing multivariate spatial dependence: model, estimate and then predict

Journal Article


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.

Publication Date


  • 2015

Citation


  • Cressie, N., Burden, S., Davis, W., Krivitsky, P. N., Mokhtarian, P., Suesse, T. & Zammit-Mangion, A. (2015). Capturing multivariate spatial dependence: model, estimate and then predict. Statistical Science: a review journal, 30 (2), 170-175.

Scopus Eid


  • 2-s2.0-84930923328

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5662

Has Global Citation Frequency


Number Of Pages


  • 5

Start Page


  • 170

End Page


  • 175

Volume


  • 30

Issue


  • 2

Place Of Publication


  • United States

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.

Publication Date


  • 2015

Citation


  • Cressie, N., Burden, S., Davis, W., Krivitsky, P. N., Mokhtarian, P., Suesse, T. & Zammit-Mangion, A. (2015). Capturing multivariate spatial dependence: model, estimate and then predict. Statistical Science: a review journal, 30 (2), 170-175.

Scopus Eid


  • 2-s2.0-84930923328

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5662

Has Global Citation Frequency


Number Of Pages


  • 5

Start Page


  • 170

End Page


  • 175

Volume


  • 30

Issue


  • 2

Place Of Publication


  • United States