Empirical likelihood estimators for the error distribution in nonparametric regression models

dc.contributor.authorKiwitt, Sebastian
dc.contributor.authorNagel, Eva­-Renate
dc.contributor.authorNeumeyer, Natalie
dc.date.accessioned2005-11-07T11:53:30Z
dc.date.available2005-11-07T11:53:30Z
dc.date.issued2005-11-07T11:53:30Z
dc.description.abstractThe aim of this paper is to show that existing estimators for the error distribution in nonparametric regression models can be improved when additional information about the distribution is included by the empirical likelihood method. The weak convergence of the resulting new estimator to a Gaussian process is shown and the performance is investigated by comparison of asymptotic mean squared errors and by means of a simulation study. As a by­product of our proofs we obtain stochastic expansions for smooth linear estimators based on residuals from the nonparametric regression model. AMS Classification: 62G08, 62G05en
dc.format.extent2428640 bytes
dc.format.extent405842 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/21671
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-14488
dc.language.isoen
dc.subjectempirical distribution functionen
dc.subjectempirical likelihooden
dc.subjecterror distributionen
dc.subjectestimating functionen
dc.subjectnonparametric regressionen
dc.subjectOwen estimatoren
dc.subject.ddc004
dc.titleEmpirical likelihood estimators for the error distribution in nonparametric regression modelsen
dc.typeText
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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