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hal.structure.identifierLaboratoire de Statistique et Probabilités [LSP]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorBERCU, Bernard
hal.structure.identifierLaboratoire de Mathématiques d'Orsay [LM-Orsay]
dc.contributor.authorPORTIER, Bruno
dc.date.accessioned2024-04-04T02:33:47Z
dc.date.available2024-04-04T02:33:47Z
dc.date.issued2008
dc.identifier.issn0363-0129
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190506
dc.description.abstractEnWe investigate the asymptotic properties of a recursive kernel density estimator associated with the driven noise of a linear regression in adaptive tracking. We provide an almost sure pointwise and uniform strong law of large numbers as well as a pointwise and multivariate central limit theorem.
dc.language.isoen
dc.publisherSociety for Industrial and Applied Mathematics
dc.subject.enAdaptive control
dc.subject.enKernel density estimation
dc.subject.enGoodness-of-fit test
dc.title.enKernel density estimation and goodness-of-fit test in adaptive tracking
dc.typeArticle de revue
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Théorie [stat.TH]
bordeaux.journalSIAM Journal on Control and Optimization
bordeaux.page2440-2457
bordeaux.volume47
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-00012682
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00012682v1
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