Optimal designs for comparing regression curves: dependence within and between groups

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dc.contributor.authorSchorning, Kirsten
dc.contributor.authorDette, Holger
dc.date.accessioned2022-06-13T13:32:57Z
dc.date.available2022-06-13T13:32:57Z
dc.date.issued2021-11-26
dc.description.abstractWe consider the problem of designing experiments for the comparison of two regression curves describing the relation between a predictor and a response in two groups, where the data between and within the group may be dependent. In order to derive efficient designs we use results from stochastic analysis to identify the best linear unbiased estimator (BLUE) in a corresponding continuous model. It is demonstrated that in general simultaneous estimation using the data from both groups yields more precise results than estimation of the parameters separately in the two groups. Using the BLUE from simultaneous estimation, we then construct an efficient linear estimator for finite sample size by minimizing the mean squared error between the optimal solution in the continuous model and its discrete approximation with respect to the weights (of the linear estimator). Finally, the optimal design points are determined by minimizing the maximal width of a simultaneous confidence band for the difference of the two regression functions. The advantages of the new approach are illustrated by means of a simulation study, where it is shown that the use of the optimal designs yields substantially narrower confidence bands than the application of uniform designs.en
dc.identifier.urihttp://hdl.handle.net/2003/40951
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22801
dc.language.isoende
dc.relation.ispartofseriesJournal of statistical theory and practice;Band 15. 2021, Heft 4, Artikel-ID 88
dc.subjectOptimal designen
dc.subjectCorrelated observationen
dc.subjectGaussian white noise modelen
dc.subjectComparison of curvesen
dc.subject.ddc310
dc.titleOptimal designs for comparing regression curves: dependence within and between groupsen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationJournal of statistical theory and practice. Band 15. 2021, Heft 4, Artikel-ID 88en
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1007/s42519-021-00218-8de

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