A new approach to optimal designs for correlated observations

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dc.contributor.authorDette, Holger
dc.contributor.authorKonstantinou, Maria
dc.contributor.authorZhigljavsky, Anatoly
dc.date.accessioned2015-11-11T14:53:15Z
dc.date.available2015-11-11T14:53:15Z
dc.date.issued2015
dc.description.abstractThis paper presents a new and effcient method for the construction of optimal designs for regression models with dependent error processes. In contrast to most of the work in this field, which starts with a model for a finite number of observations and considers the asymptotic properties of estimators and designs as the sample size converges to infinity, our approach is based on a continuous time model. We use results from stochastic anal- ysis to identify the best linear unbiased estimator (BLUE) in this model. Based on the BLUE, we construct an efficient linear estimator and corresponding optimal designs in the model for finite sample size by minimizing the mean squared error between the opti- mal solution in the continuous time model and its discrete approximation with respect to the weights (of the linear estimator) and the optimal design points, in particular in the multi-parameter case. In contrast to previous work on the subject the resulting estimators and corresponding optimal designs are very efficient and easy to implement. This means that they are practi- cally not distinguishable from the weighted least squares estimator and the corresponding optimal designs, which have to be found numerically by non-convex discrete optimization. The advantages of the new approach are illustrated in several numerical examples.en
dc.identifier.urihttp://hdl.handle.net/2003/34336
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-16413
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB 823;44/2015en
dc.subjectlinear regressionen
dc.subjectquadrature formulasen
dc.subjectDoob representationen
dc.subjectGaussian white mouse modelen
dc.subjectoptimal designen
dc.subjectcorrelated observationsen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleA new approach to optimal designs for correlated observationsen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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