Will Kleiber
Department of Statistics
University of Washington
Seattle

Title: 
Mat\'ern Cross-Covariance Functions for Multivariate Random Fields



Abstract: We introduce a flexible parametric family of matrix-valued covariance
functions for multivariate spatial random fields, where each
constituent component is a Mat\'ern process.  The model parameters are
interpretable in terms of process variance, smoothness, correlation
length, and co-located correlation coefficients, which can be positive
or negative.  Both the marginal and the cross-covariance functions are
of the Mat\'ern type.  In a data example on error fields for numerical
predictions of surface pressure and temperature over the Pacific
Northwest, a parsimonious bivariate Mat\'ern model compares favorably
to the traditional linear model of coregionalization.