Optimal design of experiments for non-linear response surface models

Many chemical and biological experiments involve multiple treatment factors and often it is convenient to fit a non-linear model in these factors. This non-linear model can be mechanistic, empirical or a hybrid of the two. Motivated by experiments in chemical engineering, we focus on D-optimal desig...

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Veröffentlicht in:Journal of the Royal Statistical Society Series C: Applied Statistics 2019-04, Vol.68 (3), p.623-640
Hauptverfasser: Huang, Yuanzhi, Gilmour, Steven G., Mylona, Kalliopi, Goos, Peter
Format: Artikel
Sprache:eng
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Zusammenfassung:Many chemical and biological experiments involve multiple treatment factors and often it is convenient to fit a non-linear model in these factors. This non-linear model can be mechanistic, empirical or a hybrid of the two. Motivated by experiments in chemical engineering, we focus on D-optimal designs for multifactor non-linear response surfaces in general. To find and study optimal designs, we first implement conventional point and co-ordinate exchange algorithms. Next, we develop a novel multiphase optimization method to construct D-optimal designs with improved properties. The benefits of this method are demonstrated by application to two experiments involving non-linear regression models. The designs obtained are shown to be considerably more informative than designs obtained by using traditional design optimality algorithms.
ISSN:0035-9254
1467-9876
DOI:10.1111/rssc.12313