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hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
dc.contributor.authorBERCU, Bernard
hal.structure.identifierLaboratoire de Mathématiques de l'INSA de Rouen Normandie [LMI]
dc.contributor.authorPORTIER, Bruno
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorVAZQUEZ, V.
dc.date.created2013
dc.date.issued2013
dc.identifier.issn0890-6327
dc.description.abstractEnA wide literature is available on the asymptotic behavior of the Durbin-Watson statistic for autoregressive models. However, it is impossible to find results on the Durbin-Watson statistic for autoregressive models with adaptive control. Our purpose is to fill the gap by establishing the asymptotic behavior of the Durbin Watson statistic for ARX models in adaptive tracking. On the one hand, we show the almost sure convergence as well as the asymptotic normality of the least squares estimators of the unknown parameters of the ARX models. On the other hand, we establish the almost sure convergence of the Durbin-Watson statistic and its asymptotic normality. Finally, we propose a bilateral statistical test for residual autocorrelation in adaptive tracking.
dc.language.isoen
dc.publisherWiley
dc.title.enOn the asymptotic behavior of the Durbin-Watson statistic for ARX processes in adaptive tracking
dc.typeArticle de revue
dc.identifier.doi10.1002/acs.2424
dc.subject.halMathématiques [math]/Probabilités [math.PR]
bordeaux.journalInternational Journal of Adaptive Control and Signal Processing
bordeaux.page1-25
bordeaux.volume27
bordeaux.peerReviewedoui
hal.identifierhal-00915951
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00915951v1
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