Careful prior specification avoids incautious inference for log-Gaussian Cox point processes
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-11-02
Print Publication Date2019-04
Permanent link to this recordhttp://hdl.handle.net/10754/629910
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AbstractHyperprior 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.
CitationS⊘rbye SH, Illian JB, Simpson DP, Burslem D, Rue H (2018) Careful prior specification avoids incautious inference for log-Gaussian Cox point processes. Journal of the Royal Statistical Society: Series C (Applied Statistics). Available: http://dx.doi.org/10.1111/rssc.12321.
SponsorsThe BCI forest dynamics research project was founded by S. P. Hubbell and R. B. Foster and is now managed by R. Condit, S. Lao and R. Perez under the Center for Tropical Forest Science and the Smithsonian Tropical Research Institute in Panama. Numerous organizations have provided funding, principally the US National Science Foundation, and hundreds of field workers have contributed. The data used can be requested and are generally granted from http://ctfs.si.edu/datarequest. Kriged estimates for concentration of the soil nutrients were downloaded from http://ctfs.si.edu/webatlas/datasets/bci/soilmaps/BCIsoil.html. We 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).