A comparison of design and model selection methods for supersaturated experiments

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Description/Abstract

Various design and model selection methods are available for supersatu-
rated designs having more factors than runs but little research is available on
their comparison and evaluation. In this paper, 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