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Regularisation and central limit theorems for an inverse problem in network sampling applications

2024-09-30Journal article Restricted access
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dc.contributor.authorAntunes, Nelson
dc.contributor.authorJacinto, Gonçalo
dc.contributor.authorPipiras, Vladas
dc.date.accessioned2024-11-13T14:16:49Z
dc.date.available2024-11-13T14:16:49Z
dc.date.issued2024-09-30
dc.description.abstractAn inverse problem motivated by packet sampling in communication networks and edge sampling in directed complex networks is studied through the operator perspective. The problem is shown to be ill-posed, with the resulting naive estimator potentially having very heavy tails, satisfying non-Gaussian central limit theorem and showing poor statistical performance. Regularisation of the problem leads to the Gaussian central limit theorem and superior performance of the regularised estimator, as a result of desirable properties of underlying operators. The limiting variance and convergence rates of the regularised estimator are also investigated. The results are illustrated on synthetic and real data from communication and complex networks.eng
dc.description.sponsorshipCNS-2319511
dc.identifier.doi10.1080/10485252.2024.2408301
dc.identifier.eissn1029-0311
dc.identifier.issn1048-5252
dc.identifier.urihttp://hdl.handle.net/10400.1/26265
dc.language.isoeng
dc.peerreviewedyes
dc.publisherTaylor and Francis Group
dc.relation.ispartofJournal of Nonparametric Statistics
dc.rights.uriN/A
dc.subjectInverse problem
dc.subjectIll-posedness
dc.subjectOperators
dc.subjectRegularisation
dc.subjectCentral limit theorem
dc.titleRegularisation and central limit theorems for an inverse problem in network sampling applicationseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleJournal of Nonparametric Statistics
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameAntunes
person.givenNameNelson
person.identifier.ciencia-idA31E-F40A-C819
person.identifier.orcid0000-0001-6071-1099
person.identifier.scopus-author-id15063869700
relation.isAuthorOfPublicationaea16f78-689b-426f-b6ad-e76e7b3972fe
relation.isAuthorOfPublication.latestForDiscoveryaea16f78-689b-426f-b6ad-e76e7b3972fe

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