Spatially varying cross-correlation coefficients in the presence of nugget effects
Type
ArticleAuthors
Kleiber, WilliamGenton, Marc G.

KAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Spatio-Temporal Statistics and Data Analysis Group
Statistics Program
Date
2012-11-29Online Publication Date
2012-11-29Print Publication Date
2013-03-01Permanent link to this record
http://hdl.handle.net/10754/562426
Metadata
Show full item recordAbstract
We derive sufficient conditions for the cross-correlation coefficient of a multivariate spatial process to vary with location when the spatial model is augmented with nugget effects. The derived class is valid for any choice of covariance functions, and yields substantial flexibility between multiple processes. The key is to identify the cross-correlation coefficient matrix with a contraction matrix, which can be either diagonal, implying a parsimonious formulation, or a fully general contraction matrix, yielding greater flexibility but added model complexity. We illustrate the approach with a bivariate minimum and maximum temperature dataset in Colorado, allowing the two variables to be positively correlated at low elevations and nearly independent at high elevations, while still yielding a positive definite covariance matrix. © 2012 Biometrika Trust.Citation
Kleiber, W., & Genton, M. G. (2012). Spatially varying cross-correlation coefficients in the presence of nugget effects. Biometrika, 100(1), 213–220. doi:10.1093/biomet/ass057Sponsors
This research was partially supported by an award made by the King Abdullah University of Science and Technology and by the National Science Foundation.Publisher
Oxford University Press (OUP)Journal
Biometrikaae974a485f413a2113503eed53cd6c53
10.1093/biomet/ass057