Design of Experiments for Screening

David C. Woods () and Susan M. Lewis ()
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David C. Woods: University of Southampton, Southampton Statistical Sciences Research Institute
Susan M. Lewis: University of Southampton, Southampton Statistical Sciences Research Institute

Chapter 33 in Handbook of Uncertainty Quantification, 2017, pp 1143-1185 from Springer

Abstract: Abstract The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs, and systematic fractional replicate designs. Generic issues of aliasing, bias, and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are addressed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modeling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared.

Keywords: Computer experiments; fractional factorial designs; Gaussian process models; group screening; space-filling designs; supersaturated designs; variable selection (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-12385-1_33

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DOI: 10.1007/978-3-319-12385-1_33

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