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

Sørbye, 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. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68(3), pp. 543-564. (doi:10.1111/rssc.12321)

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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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Illian, Professor Janine
Authors: Sørbye, S. H., Illian, J. B., Simpson, D. P., Burslem, D., and Rue, H.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of the Royal Statistical Society: Series C (Applied Statistics)
Publisher:Wiley
ISSN:0035-9254
ISSN (Online):1467-9876
Published Online:02 November 2018

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