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

Illian, J. B. , Sørbye, S. H., Rue, H. and Hendrichsen, D. K. (2012) Using INLA to fit a complex point process model with temporally varying effects - a case study. Journal of Environmental Statistics, 3(7),

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Illian, Professor Janine
Authors: Illian, J. B., Sørbye, S. H., Rue, H., and Hendrichsen, D. K.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of Environmental Statistics
Publisher:UCLA Statistics
ISSN (Online):1945-1296
Copyright Holders:Copyright © 2012 The Authors
First Published:First published in Journal of Environmental Studies 3:7
Publisher Policy:Reproduced under a Creative Commons License

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