A TOOLBOX FOR FITTING COMPLEX SPATIAL POINT PROCESS MODELS USING INTEGRATED NESTED LAPLACE APPROXIMATION (INLA)

• 成果类型: Article
• 署名作者: Illian, Janine B.; Sorbye, Sigrunn H.; Rue, Havard
• 署名单位: University of St Andrews; UiT The Arctic University of Tromso; Norwegian University of Science & Technology (NTNU)
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/11-AOAS530
• 发表日期: 2012
• 页码: 1499-1530
• 关键词: intensity estimation; inference; strategies; diversity; patterns

摘要

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.