Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 54, Issue 12, Pages 3158-3167Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2010.02.017
Keywords
Bayesian D-optimal designs; E(s(2))-optimal designs; Effect sparsity; Gauss-Dantzig selector; Main effects; Screening; Simulation
Funding
- EPSRC
- Lubrizol Corporation
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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(s(2))-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. (C) 2010 Elsevier B.V. All rights reserved.
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