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 Baerbel, Soerbye, S, Rue, H, Hendrichsen, D
Other Authors: University of St Andrews. School of Mathematics and Statistics, University of St Andrews. Scottish Oceans Institute, University of St Andrews. Centre for Research into Ecological & Environmental Modelling
Format: Article in Journal/Newspaper
Language:English
Published: 2012
Subjects:
Spatial point process
Spatial scale
Replicated patterns
QA Mathematics
QA
Greenland
Laplace
ovibos moschatus
Zackenberg
Online Access:http://hdl.handle.net/10023/3306
http://www.math.ntnu.no/inla/r-inla.org/papers/S17-2010.pdf
http://www.jenvstat.org/v03/i07/paper
Description
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. Publisher PDF Peer reviewed

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