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hal.structure.identifierLaboratoire Jean Alexandre Dieudonné [LJAD]
dc.contributor.authorRUBENTHALER, Sylvain
hal.structure.identifierAdvanced Learning Evolutionary Algorithms [ALEA]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorDEL MORAL, Pierre
hal.structure.identifierLaboratoire Jean Alexandre Dieudonné [JAD]
dc.contributor.authorPATRAS, Frédéric
dc.date.issued2011
dc.identifier.issn0894-9840
dc.description.abstractEnThe convergence of U-statistics has been intensively studied for estimators based on families of i.i.d. random variables and variants of them. In most cases, the independence assumption is crucial (Lee in Statistics: Textbooks and Monographs, vol. 10, Dekker, New York, 1990; de la Peña and Giné in Decoupling. Probability and Its Application, Springer, New York, 1999). When dealing with Feynman-Kac and other interacting particle systems of Monte Carlo type, one faces a new type of problem. Namely, in a sample of N particles obtained through the corresponding algorithms, the distributions of the particles are correlated--although any finite number of them is asymptotically independent with respect to the total number N of particles. In the present article, exploiting the fine asymptotics of particle systems, we prove convergence theorems for U-statistics in this framework
dc.language.isoen
dc.publisherSpringer
dc.title.enConvergence of U-statistics for interacting particle systems
dc.typeArticle de revue
dc.identifier.doi10.1007/s10959-011-0355-6
dc.subject.halMathématiques [math]/Probabilités [math.PR]
dc.identifier.arxiv1002.0224
bordeaux.journalJournal of Theoretical Probability
bordeaux.page1002-1027
bordeaux.volume24
bordeaux.issue4
bordeaux.peerReviewedoui
hal.identifierhal-00866889
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00866889v1
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