Capturing multivariate spatial dependence: model, estimate and then predict

Authors

Noel A. Cressie, University of WollongongFollow
Sandy Burden, University of WollongongFollow
Walter R. Davis, University of WollongongFollow
Pavel N. Krivitsky, University of WollongongFollow
Payam Mokhtarian, University of WollongongFollow
Thomas F. Suesse, University of WollongongFollow
Andrew Zammit-Mangion, University of WollongongFollow

RIS ID

98928

Publication Details

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.

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.