Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/65369
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dc.contributor.authorTamine, Julienen_US
dc.date.accessioned2012-10-19T10:25:37Z-
dc.date.available2012-10-19T10:25:37Z-
dc.date.issued2001en_US
dc.identifier.piurn:nbn:de:kobv:11-10049243en_US
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_US
dc.language.isoengen_US
dc.publisher|aHumboldt-Universität |cBerlinen_US
dc.relation.ispartofseries|aDiscussion Papers, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes |x2002,62en_US
dc.subject.jelC13en_US
dc.subject.jelC14en_US
dc.subject.jelC15en_US
dc.subject.ddc330en_US
dc.subject.keywordrobustnessen_US
dc.subject.keywordnonparametric regressionen_US
dc.subject.keywordinfluence functionen_US
dc.subject.keywordM-estimatoren_US
dc.subject.keywordL-estimatoren_US
dc.subject.keywordR-estimatoren_US
dc.subject.keywordVon-mises statistical functional generalized Delta-theoremen_US
dc.titleSmoothed influence function: Another view at robust nonparametric regressionen_US
dc.typeWorking Paperen_US
dc.identifier.ppn727037854en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:sfb373:200262-

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