Best linear unbiased estimators in continuous time regression models

dc.contributor.author	Dette, Holger
dc.contributor.author	Pepelyshev, Andrey
dc.contributor.author	Zhigljavsy, Anatoly
dc.date.accessioned	2016-10-28T12:27:26Z
dc.date.available	2016-10-28T12:27:26Z
dc.date.issued	2016
dc.description.abstract	In 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.identifier.uri	http://hdl.handle.net/2003/35304
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-17347
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;58, 2016	en
dc.subject	linear regression	en
dc.subject	continuous autoregressive model	en
dc.subject	AR processes	en
dc.subject	BLUE	en
dc.subject	optimal design	en
dc.subject	signed measures	en
dc.subject	correlated observations	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Best linear unbiased estimators in continuous time regression models	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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