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dc.contributor.authorBorrajo García, María Isabel
dc.contributor.authorGonzález Manteiga, Wenceslao
dc.contributor.authorMartínez Miranda, María Dolores
dc.date.accessioned2019-11-28T11:22:43Z
dc.date.issued2019
dc.identifier.citationBorrajo, M. I., González-Manteiga, W., & Martínez-Miranda, M. D. (2020). Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates. Computational Statistics & Data Analysis, 144
dc.identifier.issn0167-9473
dc.identifier.urihttp://hdl.handle.net/10347/20314
dc.descriptionThis is the accepted manuscript of the following article: Borrajo, M., González-Manteiga, W., & Martínez-Miranda, M. (2020). Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates. Computational Statistics & Data Analysis, 144, 106875. doi: 10.1016/j.csda.2019.106875
dc.description.abstractThe bias-variance trade-o for inhomogeneous point processes with covariates is theoretically and empirically addressed. A consistent kernel estimator for the rst-order intensity function based on covariates is constructed, which uses a convenient relationship between the intensity and the density of events location. The asymptotic bias and variance of the estimator are derived and hence the expression of its infeasible optimal bandwidth. Three data-driven bandwidth selectors are proposed to estimate the optimal bandwidth. One of them is based on a new smooth bootstrap proposal which is proved to be consistent under a Poisson assumption. The other two are a rule-of-thumb method based on assuming normallity, and a simple non-model-based approach. An extensive simulation study is accomplished considering Poisson and non-Poisson scenarios, and including a comparison with other competitors. The practicality of the new proposals is shown through an application to real data about wild res in Canada, using meteorological covariates
dc.description.sponsorshipThis work has been partially supported by the Spanish Ministry of Economy and Competitiveness, through grants number MTM2013-41383P and MTM2016-76969P, which includes support from the European Regional Development Fund (ERDF). Support from the IAP network StUDyS from Belgian Science Policy (P6/07), is also acknowledged. M.I. Borrajo has been supported by FPU grant (FPU2013/00473) from the Spanish Ministry of Education
dc.language.isoeng
dc.publisherElsevier
dc.rights© Elsevier 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/)
dc.subjectSpatial point processes
dc.subjectFirst-order intensity
dc.subjectKernel estimation
dc.subjectBandwidth selection
dc.subjectCovariates
dc.titleBootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates
dc.typeinfo:eu-repo/semantics/article
dc.identifier.DOI10.1016/j.csda.2019.106875
dc.relation.publisherversionhttps://doi.org/10.1016/j.csda.2019.106875
dc.description.embargo2021-10-22
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/embargoedAccess
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización
dc.description.peerreviewedSI


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© Elsevier 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Except where otherwise noted, this item's license is described as  © Elsevier 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (https://creativecommons.org/licenses/by-nc-nd/4.0/)





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