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December 2012 A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)
Janine B. Illian, Sigrunn H. Sørbye, Håvard Rue
Ann. Appl. Stat. 6(4): 1499-1530 (December 2012). DOI: 10.1214/11-AOAS530

Abstract

This paper develops methodology that provides a toolbox for routinely fitting complex models to realistic spatial point pattern data. We consider models that are based on log-Gaussian Cox processes and include local interaction in these by considering constructed covariates. This enables us to use integrated nested Laplace approximation and to considerably speed up the inferential task. In addition, methods for model comparison and model assessment facilitate the modelling process. The performance of the approach is assessed in a simulation study. To demonstrate the versatility of the approach, models are fitted to two rather different examples, a large rainforest data set with covariates and a point pattern with multiple marks.

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Janine B. Illian. Sigrunn H. Sørbye. Håvard Rue. "A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)." Ann. Appl. Stat. 6 (4) 1499 - 1530, December 2012. https://doi.org/10.1214/11-AOAS530

Information

Published: December 2012
First available in Project Euclid: 27 December 2012

zbMATH: 1257.62093
MathSciNet: MR3058673
Digital Object Identifier: 10.1214/11-AOAS530

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.6 • No. 4 • December 2012
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