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

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 ma...

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Bibliographic Details
Main Authors: Illian, Janine Barbel, Sørbye, Sigrunn Holbek, Rue, Håvard, Hendrichsen, Ditte Katrine
Format: Article in Journal/Newspaper
Language:English
Published: UCLA Statistics 2012
Subjects:
Greenland
Laplace
ovibos moschatus
Zackenberg
Online Access:http://eprints.gla.ac.uk/199441/
http://eprints.gla.ac.uk/199441/1/199441.pdf
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Summary: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.

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