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Optimal designs for estimating the slope of a regression

Dette, H., Melas, V. B. and Pepelyshev, Andrey 2010. Optimal designs for estimating the slope of a regression. Statistics 44 (6) , pp. 617-628. 10.1080/02331880903348473

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Abstract

In the common linear model with quantitative predictors 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Uncontrolled Keywords: locally optimal design, standardized minimax optimal design, estimating derivatives, polynomial regression, Fourier regression
Publisher: Taylor and Francis
ISSN: 0233-1888
Last Modified: 04 Jun 2017 05:09
URI: http://orca.cf.ac.uk/id/eprint/49063

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