Careful prior specification avoids incautious inference for log-Gaussian Cox point processes
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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 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.
Sørbye , S , Illian , J B , Simpson , D P , Burlsem , D & Rue , H 2019 , ' Careful prior specification avoids incautious inference for log-Gaussian Cox point processes ' , Journal of the Royal Statistical Society: Series C (Applied Statistics) , vol. 68 , no. 3 , pp. 543-564 . https://doi.org/10.1111/rssc.12321
Journal of the Royal Statistical Society: Series C (Applied Statistics)
Copyright © 2018 Royal Statistical Society. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at: https://doi.org/10.1111/rssc.12321
DescriptionWe acknowledge the principal investigators who were responsible for collecting and analysing the soil maps (Jim Dallin, Robert John, Kyle Harms, Robert Stallard and Joe Yavitt), the funding sources (National Science Foundation grants DEB021104, 021115, 0212284 and 0212818 and Office of International Science and Engineering grant 0314581, the Smithsonian Tropical Research Institute soils initiative and the Center for Tropical Forest Science) and field assistants (Paolo Segre and Juan Di Trani).
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