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Data analysis in supersaturated designs

✍ Scribed by Runze Li; Dennis K.J. Lin

Book ID
104301910
Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
116 KB
Volume
59
Category
Article
ISSN
0167-7152

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✦ Synopsis

Supersaturated designs (SSDs) can save considerable cost in industrial experimentation when many potential factors are introduced in preliminary studies. Analyzing data in SSDs is challenging because the number of experiments is less than the number of candidate factors. In this paper, we introduce a variable selection approach to identifying the active e ects in SSD via nonconvex penalized least squares. An iterative ridge regression is employed to ΓΏnd the solution of the penalized least squares. We provide both theoretical and empirical justiΓΏcations for the proposed approach. Some related issues are also discussed.

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We examine three test statistics that appear suited to analyzing supersaturated designs. All three statistics are shown to have undesirable, somewhat surprising, asymptotic power properties. Suggestions are given for improved analysis methods.