Decomposition of Variance for Spatial Cox Processes

Abdollah Jalilian, Yongtao Guan, Rasmus Waagepetersen

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Spatial Cox point processes is a natural framework for quantifying the various sources of variation governing the spatial distribution of rain forest trees. We introduce a general criterion for variance decomposition for spatial Cox processes and apply it to specific Cox process models with additive or log linear random intensity functions. We moreover consider a new and flexible class of pair correlation function models given in terms of normal variance mixture covariance functions. The proposed methodology is applied to point pattern data sets of locations of tropical rain forest trees.

Original languageEnglish (US)
Pages (from-to)119-137
Number of pages19
JournalScandinavian Journal of Statistics
Issue number1
StatePublished - Mar 2013


  • Additive random intensity
  • Composite likelihood
  • Cox process
  • Matérn covariance function
  • Normal variance mixture
  • Pair correlation function
  • Variance component

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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