Nonstationary modeling for multivariate spatial processes

AMS Citation:
Kleiber, W., and D. Nychka, 2012: Nonstationary modeling for multivariate spatial processes. Journal of Multivariate Analysis, 112, 76-91, doi:10.1016/j.jmva.2012.05.011.
Resource Type:article
Title:Nonstationary modeling for multivariate spatial processes
Abstract: We derive a class of matrix valued covariance functions where the direct and cross-covariance functions are Matérn. The parameters of the Matérn class are allowed to vary with location, yielding local variances, local ranges, local geometric anisotropies and local smoothnesses. We discuss inclusion of a nonconstant cross-correlation coefficient and a valid approximation. Estimation utilizes kernel smoothed empirical covariance matrices and a locally weighted minimum Frobenius distance that yields local parameter estimates at any location. We derive the asymptotic mean squared error of our kernel smoother and discuss the case when multiple field realizations are available. Finally, the model is illustrated on two datasets, one a synthetic bivariate one-dimensional spatial process, and the second a set of temperature and precipitation model output from a regional climate model.
Peer Review:Refereed
Copyright Information:NOTICE: This is the author's version of a work accepted for publication by Elsevier. Changes resulting from the publishing process, including peer review, editing, corrections, structural formatting and other quality control mechanisms, may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.
OpenSky citable URL: ark:/85065/d7s75hwf
Publisher's Version: 10.1016/j.jmva.2012.05.011
  • William Kleiber - NCAR/UCAR
  • Doug Nychka - NCAR/UCAR
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