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dc.contributor.authorFrancisco-Fernández, Mario
dc.contributor.authorVilar, Juan M.
dc.date.accessioned2007-06-25T17:54:32Z
dc.date.available2007-06-25T17:54:32Z
dc.date.issued2001
dc.identifier.citationCommunications in statistics, theory and methods, vol. 30, n. 7, pp. 1271-1293.es_ES
dc.identifier.issn0361-0926
dc.identifier.urihttp://hdl.handle.net/2183/856
dc.description.abstractIn this paper, we study the nonparametric estimation of the regression function and its derivatives using weighted local polynomial fitting. Consider the fixed regression model and suppose that the random observation error is coming from a strictly stationary stochastic process. Expressions for the bias and the variance array of the estimators of the regression function and its derivatives are obtained and joint asymptotic normality is established. The influence of the dependence of the data is observed in the expression of the variance. We also propose a variable bandwidth selection procedure. A simulation study and an analysis with real economic data illustrate the proposed selection method.es_ES
dc.description.sponsorshipXunta de Galicia; XUGA10501B97es_ES
dc.description.sponsorshipXunta de Galicia; PB98-0182-c02-01
dc.format.mimetypeapplication/pdf
dc.language.isoenges_ES
dc.publisherTaylor & Francises_ES
dc.relation.uri10.1081/STA-100104745es_ES
dc.rightsThis is a preprint of an article submitted for consideration in the Communications in statistics, theory and methods © 2001 copyright Taylor & Francis; Communications in statistics, theory and methods is available online at: http://www.informaworld.com/es_ES
dc.subjectNonparametric estimatorses_ES
dc.subjectLocal polynomial fittinges_ES
dc.subjectAutoregressive processes_ES
dc.titleLocal polynomial regression estimation with correlated errorses_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES


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