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

Research output: Contribution to journalArticle

DOI

Open Access Status

  • Embargoed (until 2/11/19)

Author(s)

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

School/Research organisations

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.
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Original languageEnglish
JournalJournal of the Royal Statistical Society: Series C (Applied Statistics)
VolumeEarly View
Early online date2 Nov 2018
DOIs
StateE-pub ahead of print - 2 Nov 2018

    Research areas

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

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