Premium
Careful prior specification avoids incautious inference for log‐Gaussian Cox point processes
Author(s) -
S⊘rbye Sigrunn H.,
Illian Janine B.,
Simpson Daniel P.,
Burslem David,
Rue Håvard
Publication year - 2019
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/rssc.12321
Subject(s) - hyperparameter , point process , mathematics , hyperparameter optimization , statistics , gaussian process , inference , gaussian , algorithm , computer science , artificial intelligence , physics , quantum mechanics , support vector machine
Summary 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.