Careful prior specification avoids incautious inference for log-Gaussian Cox point processes

Sigrunn H. Sørbye (Corresponding Author), Janine B. Illian, Daniel P. Simpson, David F. R. P. Burslem, Håvard Rue

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10 Citations (Scopus)
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Hyperprior specifications for random fields in spatial point process modelling
can have a major impact on the results. In fitting log-Gaussian Cox processes to rainforest tree species, we consider a reparameterised model combining a spatially structured and an unstructured random field into a single component. This component has one hyperparameter accounting for marginal variance, while an additional hyperparameter governs the fraction of the variance explained by the spatially structured effect. This facilitates interpretation of the hyperparameters and significance of covariates is studied for a range of hyperprior specifications. Appropriate scaling makes the analysis invariant to grid resolution.
Original languageEnglish
Pages (from-to)543-564
Number of pages20
JournalJournal of the royal statistical society series c-Applied statistics
Issue number3
Early online date2 Nov 2018
Publication statusPublished - Apr 2019


  • Bayesian analysis
  • spatial point process
  • penalised complexity prior
  • R-INLA
  • Spatial modelling
  • Penalized complexity prior
  • Spatial point process


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