Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation
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https://doi.org/10.1016/j.spasta.2019.100400 |
Metadatos
Título
Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimationFecha de publicación
2020Editor
ElsevierISSN
2211-6753Cita bibliográfica
MATEU, Jorge; MORADI, Mehdi; CRONIE, Ottmar. Spatio-temporal point patterns on linear networks: Pseudo-separable intensity estimation. Spatial Statistics, 2020, vol. 37, p. 100400Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.sciencedirect.com/science/article/pii/S2211675319301514#!Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Aside from reviewing different intensity estimation schemesfor point processes on linear networks, this paper introducestwo Voronoi-based intensity estimation approaches for spatio-temporal linear network point ... [+]
Aside from reviewing different intensity estimation schemesfor point processes on linear networks, this paper introducestwo Voronoi-based intensity estimation approaches for spatio-temporal linear network point processes. The first is a separableestimator, which is obtained as a scaled product of a resample-smoothed Voronoi intensity estimator on the linear network inquestion and another one on the time domain. The second one,which we refer to as a pseudo-separable resample-smoothedVoronoi intensity estimator, uses a slightly different thinningstrategy.Throughasimulationstudyweshowthatthelatterper-forms slightly better than the former. We finally apply the latterestimator to a spatio-temporal traffic accident point pattern. [-]
Publicado en
Spatial Statistics, 2020, vol. 37, p. 100400Proyecto de investigación
J. Mateu is funded by Grant MTM2016-78917-R from the Spanish Ministry of Economy andCompetitivity.Derechos de acceso
© 2020 Elsevier B.V. All rights reserved.
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