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Capturing Multivariate Spatial Dependence: Model, Estimate and then Predict
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. [arXiv:1507.08017].Comment: Published at http://dx.doi.org/10.1214/15-STS517 in the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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