Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/22247
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dc.contributor.authorIoannides, D. A.en_US
dc.contributor.authorMatzner-Lober, E.en_US
dc.date.accessioned2009-01-29T14:55:05Z-
dc.date.available2009-01-29T14:55:05Z-
dc.date.issued2003en_US
dc.identifier.piurn:nbn:de:kobv:11-10050356-
dc.identifier.urihttp://hdl.handle.net/10419/22247-
dc.description.abstractIn a lot of situations, variables are measured with errors. While this problem has been previously studied in the kontext of kernel regression, no work has been done in quantile regression. To estimate this function we use deconvoluting kernel estimators. The asymptotic behaviour of these estimators depends on the smoothness of the noise distribution.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseries|aDiscussion papers of interdisciplinary research project 373 |x2003,32en_US
dc.subject.ddc330en_US
dc.subject.stwRegressionen_US
dc.subject.stwSchätztheorieen_US
dc.subject.stwTheorieen_US
dc.titleRegression quantiles with errors-in-variablesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn379165465en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:sfb373:200332-

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