A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA)

Illian, J. B. , Sørbye, S. H. and Rue, H. (2012) A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA). Annals of Applied Statistics, 6(4), pp. 1499-1530. (doi:10.1214/11-AOAS530)

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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.

Item Type:Articles
Additional Information:The authors also gratefully acknowledge the financial support of Research Councils UK for Illian.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Illian, Professor Janine
Authors: Illian, J. B., Sørbye, S. H., and Rue, H.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Annals of Applied Statistics
Publisher:Institute of Mathematical Statistics
ISSN:1932-6157
ISSN (Online):1941-7330
Copyright Holders:Copyright © 2012 Institute of Mathematical Statistics
First Published:First published in Annals of Applied Statistics 6:1499-1530
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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