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dc.contributor.authorDette, Holger-
dc.contributor.authorPepelyshev, Andrey-
dc.contributor.authorZhigljavsy, Anatoly-
dc.date.accessioned2016-10-28T12:27:26Z-
dc.date.available2016-10-28T12:27:26Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/2003/35304-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17347-
dc.description.abstractIn this paper the problem of best linear unbiased estimation is investigated for continuous-time regression models. We prove several general statements concerning the explicit form of the best linear unbiased estimator (BLUE), in particular when the error process is a smooth process with one or several derivatives of the response process available for construction of the estimators. We derive the explicit form of the BLUE for many specific models including the cases of continuous autoregressive errors of order two and integrated error processes (such as integrated Brownian motion). The results are illustrated by several examples.en
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;58, 2016en
dc.subjectlinear regressionen
dc.subjectcontinuous autoregressive modelen
dc.subjectAR processesen
dc.subjectBLUEen
dc.subjectoptimal designen
dc.subjectsigned measuresen
dc.subjectcorrelated observationsen
dc.subject.ddc310-
dc.subject.ddc330-
dc.subject.ddc620-
dc.titleBest linear unbiased estimators in continuous time regression modelsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 823

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