Nonparametric M-estimation with long-memory errors

dc.contributor.author	Ghosh, Sucharita	de
dc.contributor.author	Sibbertsen, Philipp	de
dc.date.accessioned	2004-12-06T18:42:49Z
dc.date.available	2004-12-06T18:42:49Z
dc.date.issued	2000	de
dc.description.abstract	We 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.extent	244852 bytes
dc.format.extent	308145 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/5041
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15124
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject.ddc	310	de
dc.title	Nonparametric M-estimation with long-memory errors	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access
eldorado.dnb.deposit	true

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