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

Janine B. Illian, Sigrunn H. Sørbye, Haavard Rue

Research output: Contribution to journalArticlepeer-review

90 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1499-1530
Number of pages32
JournalAnnals of Applied Statistics
Volume6
Issue number4
DOIs
StatePublished - Jan 1 2012

Keywords

  • Cox processes
  • Marked point patterns
  • Model assessment
  • Model comparison

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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