The Annals of Applied Statistics

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

Janine B. Illian, Sigrunn H. Sørbye, and Håvard Rue

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat. Volume 6, Number 4 (2012), 1499-1530.

Dates
First available in Project Euclid: 27 December 2012

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1356629049

Digital Object Identifier
doi:10.1214/11-AOAS530

Mathematical Reviews number (MathSciNet)
MR3058673

Zentralblatt MATH identifier
06141537

Keywords
Cox processes marked point patterns model assessment model comparison

Citation

Illian, Janine B.; Sørbye, Sigrunn H.; Rue, Håvard. A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA). Ann. Appl. Stat. 6 (2012), no. 4, 1499--1530. doi:10.1214/11-AOAS530. http://projecteuclid.org/euclid.aoas/1356629049.


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