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

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

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 (US)
Pages (from-to)543-564
Number of pages22
JournalJournal of the Royal Statistical Society: Series C (Applied Statistics)
Volume68
Issue number3
DOIs
StatePublished - Nov 2 2018

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