S-estimation in the nonlinear regression model with long-memory error terms

dc.contributor.author	Sibbertsen, Philipp	de
dc.date.accessioned	2004-12-06T18:39:44Z
dc.date.available	2004-12-06T18:39:44Z
dc.date.issued	1999	de
dc.description.abstract	In this paper we consider the asymptotic distribution of S-estimators in the nonlinear regression model with long-memory error terms. S-estimators are robust estimates with a high breakdown point and good asymptotic properties in the iid case. They are constructed for linear regression. In the nonlinear regression model with long-memory errors it turns out, that S-estimators are asymptotically normal with a rate of convergence of n^1-H , 1/2<H<1. But the distribution depends heavily on the unknown parameter vector.	en
dc.format.extent	153170 bytes
dc.format.extent	336459 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4923
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-5408
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	long-range dependence	en
dc.subject	nonlinear regression model	en
dc.subject	robustness	en
dc.subject.ddc	310	de
dc.title	S-estimation in the nonlinear regression model with long-memory error terms	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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