Statistics and Data Science Seminar
Juan Du
Kansas State University
Compactly Supported Multivariate Covariance Modeling with Application to Spatial Taper
Abstract: We derive several classes of covariance matrix functions whose entries are
compactly supported. These compactly supported matrix functions are used as building
blocks to formulate other covariance matrix functions for modeling of multivariate
spatial processes. In particular, a multivariate version of the celebrated spherical
model is produced, as well as a class of second-order multivariate stochastic processes
whose direct and cross covariance functions are of Pólya type. On the other hand, by
employing some of the proposed compactly supported correlation matrix functions as the
tapering matrix function, we study the multivariate generalization of the spatial
covariance tapering technique, which is useful to mitigate the numerical burdens in
dealing with the large spatial data sets by making covariance matrices sparse.
Simulation study is conducted to show the computational efficiency and application in
spatial prediction by using proposed multivariate tapering technique.
Wednesday March 14, 2012 at 4:00 PM in SEO 636