Using INLA to fit a complex point process model with temporally varying effects – a case study

Janine Baerbel Illian, S Soerbye, H Rue, D Hendrichsen

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Abstract

Integrated nested Laplace approximation (INLA) provides a fast and yet quite exact approach to fitting complex latent Gaussian models which comprise many statistical models in a Bayesian context, including log Gaussian Cox processes. This paper discusses how a joint log Gaussian Cox process model may be fitted to independent replicated point patterns. We illustrate the approach by fitting a model to data on the locations of muskoxen (Ovibos moschatus) herds in Zackenberg valley, Northeast Greenland and by detailing how this model is specified within the R-interface R-INLA. The paper strongly focuses on practical problems involved in the modelling process, including issues of spatial scale, edge effects and prior choices, and finishes with a discussion on models with varying boundary conditions.
Original languageEnglish
JournalJournal of Environmental Statistics
Volume3
Issue number7
Publication statusPublished - Jul 2012

Keywords

  • Spatial point process
  • Spatial scale
  • Replicated patterns

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