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Second-order preserving point process permutations
dc.contributor.author | Mohler, George | |
dc.contributor.author | Mateu, Jorge | |
dc.date.accessioned | 2023-06-08T15:16:53Z | |
dc.date.available | 2023-06-08T15:16:53Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | MOHLER, George; MATEU, Jorge. Second‐order preserving point process permutations. Stat, 2023, vol. 12, núm. 1, p. e558 | ca_CA |
dc.identifier.issn | 2049-1573 | |
dc.identifier.uri | http://hdl.handle.net/10234/202760 | |
dc.description.abstract | While random permutations of point processes are useful for generating counterfactuals in bivariate interaction tests, such permutations require that the underlying intensity be separable. In many real-world datasets where clustering or inhibition is present, such an assumption does not hold. Here, we introduce a simple combinatorial optimization algorithm that generates second-order preserving (SOP) point process permutations, for example, permutations of the times of events such that the function of the permuted process matches the function of the data. We apply the algorithm to synthetic data generated by a self-exciting Hawkes process and a self-avoiding point process, along with data from Los Angeles on earthquakes and arsons and data from Indianapolis on law enforcement drug seizures and overdoses. In all cases, we are able to generate a diverse sample of permuted point processes where the distribution of the functions closely matches that of the data. We then show how SOP point process permutations can be used in two applications: (1) bivariate Knox tests and (2) data augmentation to improve deep learning-based space-time forecasts. | ca_CA |
dc.format.extent | 11 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Wiley | ca_CA |
dc.relation.isPartOf | Stat, 2023, vol. 12, núm. 1, p. e558 | ca_CA |
dc.rights | This is an open access article under the terms of theCreative Commons AttributionLicense, which permits use, distribution and reproduction in any medium,provided the original work is properly cited.© 2023 The Authors.Statpublished by John Wiley & Sons Ltd | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | bivariate point processes | ca_CA |
dc.subject | data augmentation | ca_CA |
dc.subject | independence | ca_CA |
dc.subject | Knox test | ca_CA |
dc.subject | permutations | ca_CA |
dc.subject | second-order characteristics | ca_CA |
dc.subject | self-exciting point processes | ca_CA |
dc.title | Second-order preserving point process permutations | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1002/sta4.558 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://onlinelibrary.wiley.com/doi/10.1002/sta4.558 | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
oaire.awardNumber | SCC-2125319 | ca_CA |
oaire.awardNumber | ATD-2124313 | ca_CA |
oaire.awardNumber | R01CE003362 | ca_CA |
oaire.awardNumber | FA9550-22-1-0380 | ca_CA |
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Excepto si se señala otra cosa, la licencia del ítem se describe como: This is an open access article under the terms of theCreative Commons AttributionLicense, which permits use, distribution and reproduction in any medium,provided the original work is properly cited.© 2023 The Authors.Statpublished by John Wiley & Sons Ltd