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

Sigrunn H. Sørbye; Janine B. Illian; Daniel P. Simpson; David F. R. P. Burslem; Håvard Rue

doi:10.1111/rssc.12321

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

Sigrunn H. Sørbye (Corresponding Author), Janine B. Illian, Daniel P. Simpson, David F. R. P. Burslem, Håvard Rue

Aberdeen Centre For Environmental Sustainability

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

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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 hyperparameter accounting for marginal variance, while an additional hyperparameter governs the fraction of the variance 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 language	English
Pages (from-to)	543-564
Number of pages	20
Journal	Journal of the royal statistical society series c-Applied statistics
Volume	68
Issue number	3
Early online date	2 Nov 2018
DOIs	https://doi.org/10.1111/rssc.12321
Publication status	Published - Apr 2019

Bibliographical note

The 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 in Panama. Numerous organizations have provided funding, principally the U.S. National Science Foundation, and hundreds of field workers have contributed. The data used can be requested and generally granted at http://ctfs.si.edudatarequest. 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 that were responsible for collecting and analysing the soil maps (Jim Dallin, Robert John, Kyle Harms, Robert Stallard and Joe Yavitt), the funding sources (NSF DEB021104,021115, 0212284,0212818 and OISE 0314581, STRI Soils Initiative and CTFS) and field assistants (Paolo Segre and Juan Di Trani).

Keywords

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

Access to Document

10.1111/rssc.12321Licence: Unspecified

Accepted Manuscript
This is the peer reviewed version of the following article:S⊘rbye, S. H., Illian, J. B., Simpson, D. P., Burslem, D. and Rue, H. (2019), Careful prior specification avoids incautious inference for log‐Gaussian Cox point processes. J. R. Stat. Soc. C, 68: 543-564., which has been published in final form at https://doi.org/10.1111/rssc.12321. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Accepted author manuscript, 1.44 MBLicence: Unspecified

Cite this

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title = "Careful prior specification avoids incautious inference for log-Gaussian Cox point processes",

abstract = "Hyperprior specifications for random fields in spatial point process modellingcan 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 hyperparameter accounting for marginal variance, while an additional hyperparameter governs the fraction of the variance 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.",

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

Abstract

Bibliographical note

Keywords

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