Bayesian D-optimal supersaturated designs |
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| Authors: | Bradley Jones Dennis KJ Lin Christopher J Nachtsheim |
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| Institution: | 1. SAS Institute, Cary, NC 27513, USA;2. Department of Supply Chain and Information Systems, The Pennsylvania State University, University Park, PA 16802, USA;3. Carlson School of Management, University of Minnesota, Minneapolis, MN 55455, USA |
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| Abstract: | We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E(s2) criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks. |
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| Keywords: | Blocking Exchange algorithm Model robust designs Ridge regression Screening designs |
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