Using INLA to fit a complex point process model with temporally varying effects – a case study
MetadataShow full item record
Altmetrics Handle Statistics
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.
Illian , J B , Soerbye , S , Rue , H & Hendrichsen , D 2012 , ' Using INLA to fit a complex point process model with temporally varying effects – a case study ' , Journal of Environmental Statistics , vol. 3 , no. 7 .
Journal of Environmental Statistics
(c) 2012 The authors. This is an open access article available under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) which permits anyone to download, reuse, reprint, modify, distribute, and/or copy articles in Journal of Environmental Statistics, so long as the original authors and source are credited.
Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.