Optimal designs for regression models with autoregressive errors structure

dc.contributor.authorDette, Holger
dc.contributor.authorPepelyshev, Andrey
dc.contributor.authorZhigljavsky, Anatoly
dc.date.accessioned2016-02-15T12:56:47Z
dc.date.available2016-02-15T12:56:47Z
dc.date.issued2016
dc.description.abstractIn the one-parameter regression model with AR(1) and AR(2) errors we find explicit expressions and a continuous approximation of the optimal discrete design for the signed least square estimator. The results are used to derive the optimal variance of the best linear estimator in the continuous time model and to construct efficient estimators and corresponding optimal designs for finite samples. The resulting procedure (estimator and design) provides nearly the same efficiency as the weighted least squares and its variance is close to the optimal variance in the continuous time model. The results are illustrated by several examples demonstrating the feasibility of our approach.en
dc.identifier.urihttp://hdl.handle.net/2003/34512
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-16565
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;10/2016en
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.titleOptimal designs for regression models with autoregressive errors structureen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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