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

Sigrunn Sørbye, Janine B. Illian, Daniel P. Simpson, David Burlsem, Håvard Rue

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)
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Abstract

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 hyperpa- rameter accounting for marginal variance, while an additional hyperparameter governs the fraction of the variance explained by the spatially structured effect. This facilitates inter- pretation 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
JournalJournal of the Royal Statistical Society: Series C (Applied Statistics)
Volume68
Issue number3
Early online date2 Nov 2018
DOIs
Publication statusPublished - Apr 2019

Keywords

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

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