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Rotation designs: orthogonal first‐order designs with higher order projectivity

Rotation designs: orthogonal first‐order designs with higher order projectivity In many factorial experiments, just a few of the experimental factors account for most of the variation in the response, a situation known as factor sparsity. Accurate modelling of the factor–response relationship may require use of higher‐order terms in the active factors. In such settings, it may be desirable to use a design that is able, simultaneously, to screen out the important factors and to fit higher‐order models in those factors. We derive a useful class of designs by rotating standard two‐level fractional factorials. A special class of rotations is developed that has some appealing symmetry properties and can accommodate more factors than the rotation designs in Bursztyn and Steinberg (J. Stat. Plann. Inference 2001;97:399). A comparison of designs based on their projection properties and alias matrices shows that the new designs are better than many other alternatives. Copyright © 2002 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Rotation designs: orthogonal first‐order designs with higher order projectivity

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References (21)

Publisher
Wiley
Copyright
Copyright © 2002 John Wiley & Sons, Ltd.
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.473
Publisher site
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Abstract

In many factorial experiments, just a few of the experimental factors account for most of the variation in the response, a situation known as factor sparsity. Accurate modelling of the factor–response relationship may require use of higher‐order terms in the active factors. In such settings, it may be desirable to use a design that is able, simultaneously, to screen out the important factors and to fit higher‐order models in those factors. We derive a useful class of designs by rotating standard two‐level fractional factorials. A special class of rotations is developed that has some appealing symmetry properties and can accommodate more factors than the rotation designs in Bursztyn and Steinberg (J. Stat. Plann. Inference 2001;97:399). A comparison of designs based on their projection properties and alias matrices shows that the new designs are better than many other alternatives. Copyright © 2002 John Wiley & Sons, Ltd.

Journal

Applied Stochastic Models in Business and Industry – Wiley

Published: Jul 1, 2002

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