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A comparison of design and model selection methods for supersaturated experiments

Marley, Christopher J. and Woods, David C. (2010) A comparison of design and model selection methods for supersaturated experiments Computational Statistics & Data Analysis, 54, (12), pp. 3158-3167. (doi:10.1016/j.csda.2010.02.017).

Record type: Article


Various design and model selection methods are available for supersaturated designs having more factors than runs but little research is available on their comparison and evaluation. Simulated experiments are used to evaluate the use of E(s2)-optimal and Bayesian D-optimal designs and to compare three analysis strategies representing regression, shrinkage and a novel model-averaging procedure. Suggestions are made for choosing the values of the tuning constants for each approach. Findings include that (i) the preferred analysis is via shrinkage; (ii) designs with similar numbers of runs and factors can be effective for a considerable number of active effects of only moderate size; and (iii) unbalanced designs can perform well. Some comments are made on the performance of the design and analysis methods when effect sparsity does not hold.

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e-pub ahead of print date: 2 March 2010
Published date: 1 December 2010
Keywords: bayesian D-optimal designs, E(s2)-optimal designs, effect sparsity, gauss–dantzig selector, main effects, screening, simulation
Organisations: Southampton Statistical Research Inst.


Local EPrints ID: 151259
ISSN: 0167-9473
PURE UUID: 0cf59c04-b230-46af-a82d-898dfd3b8702

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Date deposited: 10 May 2010 09:23
Last modified: 18 Jul 2017 12:56

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Author: Christopher J. Marley
Author: David C. Woods

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