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    Spatially varying cross-correlation coefficients in the presence of nugget effects

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    Type
    Article
    Authors
    Kleiber, William
    Genton, Marc G. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Spatio-Temporal Statistics and Data Analysis Group
    Statistics Program
    Date
    2012-11-29
    Online Publication Date
    2012-11-29
    Print Publication Date
    2013-03-01
    Permanent link to this record
    http://hdl.handle.net/10754/562426
    
    Metadata
    Show full item record
    Abstract
    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/ass057
    Sponsors
    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
    Biometrika
    DOI
    10.1093/biomet/ass057
    ae974a485f413a2113503eed53cd6c53
    10.1093/biomet/ass057
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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