S - estimators in the linear regression model with long - memory error terms

dc.contributor.author	Sibbertsen, Philipp	de
dc.date.accessioned	2004-12-06T18:38:13Z
dc.date.available	2004-12-06T18:38:13Z
dc.date.issued	1998	de
dc.description.abstract	We investigate the behaviour of S - estimators in the linear regression model, when the error terms are long - memory Gaussian processes. It turns out that under mild regularity conditions S - estimators are still normally distributed with a similar variance - covariance structure as in the i.i.d. case. This assertion holds for the parameter estimates as well as for the scale estimates. Also the rate of convergence is for S - estimators the same as for the least squares estimator and for the BLUE.	en
dc.format.extent	170111 bytes
dc.format.extent	365961 bytes
dc.format.mimetype	application/pdf
dc.format.mimetype	application/postscript
dc.identifier.uri	http://hdl.handle.net/2003/4831
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-5407
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	linear regression model	en
dc.subject	long - range dependence	en
dc.subject	robustness	en
dc.subject.ddc	310	de
dc.title	S - estimators in the linear regression model with long - memory error terms	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access
eldorado.dnb.deposit	true

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