Analysing Multivariate Spatial Point Processes with Continuous Marks: A Graphical Modelling Approach
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https://doi.org/10.1111/insr.12272 |
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Título
Analysing Multivariate Spatial Point Processes with Continuous Marks: A Graphical Modelling ApproachFecha de publicación
2019-04Editor
Wiley; International Statistical InstituteCita bibliográfica
ECKARDT, Matthias; MATEU, Jorge. Analysing multivariate spatial point processes with continuous marks: A graphical modelling approach. International Statistical Review, 2019, 87.1: 44-67.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://onlinelibrary.wiley.com/doi/full/10.1111/insr.12272Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
This paper discusses the application of graphical modelling in the statistical analysis of marked point patterns. We consider a multivariate planar point process with quantitative marks. After a survey of statistical ... [+]
This paper discusses the application of graphical modelling in the statistical analysis of marked point patterns. We consider a multivariate planar point process with quantitative marks. After a survey of statistical methods for marked point processes, a new graphical model is presented. The sub‐processes of marked points with identical discrete marks are identified with nodes of a graph, which is used to describe aspects of the spatial relationship: If two sub‐patterns are similar, then an arc is made between the corresponding nodes. Similarity is defined based on spectral densities, which makes the computations efficient. The resulting graph presents all these pairwise similarities simultaneously. We demonstrate the application of our method in the analysis of a multi‐species forest, where the points are tree locations, the discrete marks tree species and the quantitative marks are the diameters at breast height. [-]
Proyecto de investigación
Spanish Ministry of Economy and Competitiveness (Grant Number: MTM2016-78917-R)Derechos de acceso
© 2018 The Authors. International Statistical Review © 2018 International Statistical Institute
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