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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.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
dc.contributor.authorPROIA, Frédéric
dc.date.created2011
dc.date.issued2013
dc.identifier.issn1292-8100
dc.description.abstractEnThe purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin-Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic normality for both the least squares estimator of the unknown parameter of the autoregressive process as well as for the serial correlation estimator associated to the driven noise. In addition, the almost sure rates of convergence of our estimates are also provided. It allows us to establish the almost sure convergence and the asymptotic normality for the Durbin-Watson statistic. Finally, we propose a new bilateral statistical test for residual autocorrelation.
dc.language.isoen
dc.publisherEDP Sciences
dc.subject.enDurbin-Watson statistic
dc.subject.enAutoregressive process
dc.subject.enResidual autocorrelation
dc.subject.enStatistical test for serial correlation
dc.title.enA sharp analysis on the asymptotic behavior of the Durbin-Watson statistic for the first-order autoregressive process
dc.typeArticle de revue
dc.identifier.doi10.1051/ps/2012005
dc.subject.halMathématiques [math]/Probabilités [math.PR]
bordeaux.journalESAIM: Probability and Statistics
bordeaux.page500-530
bordeaux.volume17
bordeaux.peerReviewedoui
hal.identifierhal-00642634
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00642634v1
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