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

Sigrunn H. S⊘rbye, Janine B. Illian, Daniel P. Simpson, David Burslem, Håvard Rue

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

Abstract

Hyperprior specifications for random fields in spatial point process modelling can have a major influence on the results. In fitting log-Gaussian Cox processes to rainforest tree species, we consider a reparameterized model combining a spatially structured and an unstructured random field into a single component. This component has one hyperparameter accounting for marginal variance, whereas an additional hyperparameter governs the fraction of the variance that is 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 pages22
JournalJournal of the Royal Statistical Society Series C-Applied Statistics
Volume68
Issue number3
DOIs
Publication statusPublished - Apr 2019
Externally publishedYes

Keywords

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

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