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dc.contributor.authorTamine, Julienen
dc.date.accessioned2012-10-19T10:25:37Z-
dc.date.available2012-10-19T10:25:37Z-
dc.date.issued2001-
dc.identifier.piurn:nbn:de:kobv:11-10049243en
dc.identifier.urihttp://hdl.handle.net/10419/65369-
dc.description.abstractIn this work, we introduce a smoothed influence function that constitute a theoretical tool for studying the outliers robustness properties of a large class of nonparametric estimators. With this tool, we first show the nonrobustness of the Nadaraya-Watson estimator of regression. Then we show that the M, the L and the R-estimators of the regression achieve robustness (when estimated by kernel). Our results are illustrated performing Monte-Carlo simulation.en
dc.language.isoengen
dc.publisher|aHumboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes |cBerlinen
dc.relation.ispartofseries|aSFB 373 Discussion Paper |x2002,62en
dc.subject.jelC13en
dc.subject.jelC14en
dc.subject.jelC15en
dc.subject.ddc330en
dc.subject.keywordrobustnessen
dc.subject.keywordnonparametric regressionen
dc.subject.keywordinfluence functionen
dc.subject.keywordM-estimatoren
dc.subject.keywordL-estimatoren
dc.subject.keywordR-estimatoren
dc.subject.keywordVon-mises statistical functional generalized Delta-theoremen
dc.titleSmoothed influence function: Another view at robust nonparametric regression-
dc.typeWorking Paperen
dc.identifier.ppn727037854en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:sfb373:200262en

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