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dc.contributor.authorDette, Holgeren_US
dc.contributor.authorMelas, Viatcheslav B.en_US
dc.description.abstractIn the common linear regression model we consider the problem of designing experiments for estimating the slope of the expected response in a regression. We discuss locally optimal designs, where the experimenter is only interested in the slope at a particular point, and standardized minimax optimal designs, which could be used if precise estimation of the slope over a given region is required. General results on the number of support points of locally optimal designs are derived if the regression functions form a Chebyshev system. For polynomial regression and Fourier regression models of arbitrary degree the optimal designs for estimating the slope of the regression are determined explicitly for many cases of practical interest.en_US
dc.publisher|aUniv., SFB 475|cDortmunden_US
dc.relation.ispartofseries|aTechnical Report // Sonderforschungsbereich 475, Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund|x2008,21en_US
dc.subject.keywordlocally optimal designen_US
dc.subject.keywordstandardized minimax optimal designen_US
dc.subject.keywordestimating derivativesen_US
dc.subject.keywordpolynomial regressionen_US
dc.subject.keywordFourier regressionen_US
dc.titleOptimal designs for estimating the slope of a regressionen_US
dc.type|aWorking Paperen_US

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