Nonparametric M-estimation with long-memory errors

dc.contributor.authorGhosh, Sucharitade
dc.contributor.authorSibbertsen, Philippde
dc.date.accessioned2004-12-06T18:42:49Z
dc.date.available2004-12-06T18:42:49Z
dc.date.issued2000de
dc.description.abstractWe investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind this study is illustrated with an example.de
dc.format.extent244852 bytes
dc.format.extent308145 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/2003/5041
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-15124
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subject.ddc310de
dc.titleNonparametric M-estimation with long-memory errorsen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access

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