Statistics and Computing

, Volume 20, Issue 3, pp 283–293 | Cite as

Weighted composite likelihood-based tests for space-time separability of covariance functions

  • M. Bevilacqua
  • J. MateuEmail author
  • E. Porcu
  • H. Zhang
  • A. Zini


Testing for separability of space-time covariance functions is of great interest in the analysis of space-time data. In this paper we work in a parametric framework and consider the case when the parameter identifying the case of separability of the associated space-time covariance lies on the boundary of the parametric space. This situation is frequently encountered in space-time geostatistics. It is known that classical methods such as likelihood ratio test may fail in this case.

We present two tests based on weighted composite likelihood estimates and the bootstrap method, and evaluate their performance through an extensive simulation study as well as an application to Irish wind speeds. The tests are performed with respect to a new class of covariance functions, which presents some desirable mathematical features and has margins of the Generalized Cauchy type. We also apply the test on a element of the Gneiting class, obtaining concordant results.


Fractal dimension Full symmetry Hurst effect Space-time covariance functions Space-time separability Weighted composite likelihood 


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • M. Bevilacqua
    • 1
  • J. Mateu
    • 2
    Email author
  • E. Porcu
    • 2
  • H. Zhang
    • 3
  • A. Zini
    • 4
  1. 1.Dipartimento di Scienze StatisticheUniversità di PadovaPadovaItaly
  2. 2.Department of MathematicsUniversitat Jaume ICastellónSpain
  3. 3.Department of StatisticsPurdue UniversityWest LafayetteUSA
  4. 4.Department of Quantitative Methods for Business and Economic SciencesUniversity of Milano BicoccaMilanItaly

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