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dc.contributor.authorBorrajo García, María Isabel
dc.contributor.authorComas Rodríguez, Carles
dc.contributor.authorCostafreda-Aumedes, Sergi
dc.contributor.authorMateu, Jorge
dc.date.accessioned2022-08-03T08:07:42Z
dc.date.available2022-08-03T08:07:42Z
dc.date.issued2021
dc.identifier.citationStochastic Environmental Research and Risk Assessment 36, 1563–1577 (2022). https://doi.org/10.1007/s00477-021-02072-3
dc.identifier.urihttp://hdl.handle.net/10347/28996
dc.description.abstractWildlife-vehicle collisions on road networks represent a natural problem between human populations and the environment, that affects wildlife management and raise a risk to the life and safety of car drivers. We propose a statistically principled method for kernel smoothing of point pattern data on a linear network when the first-order intensity depends on covariates. In particular, we present a consistent kernel estimator for the first-order intensity function that uses a convenient relationship between the intensity and the density of events location over the network, which also exploits the theoretical relationship between the original point process on the network and its transformed process through the covariate. We derive the asymptotic bias and variance of the estimator, and adapt some data-driven bandwidth selectors to estimate the optimal bandwidth. The performance of the estimator is analysed through a simulation study under inhomogeneous scenarios. We present a real data analysis on wildlife-vehicle collisions in a region of North-East of Spain
dc.description.sponsorshipOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature
dc.language.isoeng
dc.publisherSpringer
dc.rights© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
dc.rightsAtribución 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectBandwidth selection
dc.subjectCovariates
dc.subjectFirst-order intensity
dc.subjectKernel estimation
dc.subjectLinear network
dc.subjectSpatial point pattern
dc.subjectWildlife-vehicle accidents
dc.titleStochastic smoothing of point processes for wildlife-vehicle collisions on road networks
dc.typeinfo:eu-repo/semantics/article
dc.identifier.DOI10.1007/s00477-021-02072-3
dc.relation.publisherversionhttps://doi.org/10.1007/s00477-021-02072-3
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.e-issn1436-3259
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Matemáticas
dc.description.peerreviewedSI


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© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as  © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/





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