Supersaturated designs with high searching probability

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• 作者：Kashinath Chatterjee ; Angshuman Sarkar ; Dennis K.J. Lin
• 关键词：Design matrix ; Effect sparsity ; Screening experiment
• 刊名：Journal of Statistical Planning and Inference
• 出版年：2008
• 出版时间：1 January 2008
• 年：2008
• 卷：138
• 期：1
• 页码：272-277
• 全文大小：144 K

文摘

A supersaturated design is essentially a fractional factorial design whose number of experimental variables is greater than or equal to its number of experimental runs. Under the effect sparsity assumption, a supersaturated design can be very cost-effective. In this paper, our prime objective is to compare the existing two-level supersaturated designs for the noisy case through the probability of correct searching—a powerful criterion proposed by Shirakura et al. [1996. Searching probabilities for nonzeroeffects in search designs for the noisy case. Ann. Statist. 24, 2560–2568]. An algorithm is proposed to construct supersaturated designs with high probability of correct searching. Examples are given for illustration.