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dc.contributor.authorVilar, Juan M.
dc.contributor.authorVilar, José
dc.date.accessioned2007-06-25T17:31:57Z
dc.date.available2007-06-25T17:31:57Z
dc.date.issued2000
dc.identifier.citationTest, vol. 9, n. 1, pp. 209-232es_ES
dc.identifier.issn1133-0686
dc.identifier.urihttp://hdl.handle.net/2183/854
dc.description.abstractIn the case of the random design nonparametric regression, one recursive local polynomial smoother is considered. Expressions for the bias and the variance matrix of the estimators of the regression function and its derivatives are obtained under dependence conditions (strongly mixing processes). The obtained Mean Squared Error is shown to be larger than those of the analogous nonrecursive regression estimators, although retaining the same convergence rate. The properties of strong consistency with convergence rates are established for the proposed estimators. Finally, in order to analyse the influence of both the sample size and the dependence in the behaviour of the proposed recursive estimator, a simulation study is performed.es_ES
dc.description.sponsorshipMinisterio de Ciencia y Tecnología; PB98-0182-C02-01es_ES
dc.description.sponsorshipGalicia. Consellería de Educación e Ordenación Universitaria; XUGA10501B97
dc.format.mimetypeapplication/pdf
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.uri10.1007/BF02595859es_ES
dc.rightsThe original publication is available at www.springerlink.comes_ES
dc.subjectLocal polynomial fittinges_ES
dc.subjectRecursive nonparametric estimationes_ES
dc.subjectStrongly mixing processes_ES
dc.titleRecursive local polynomial regression under dependence conditionses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES


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