Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416353 | Computational Statistics & Data Analysis | 2006 | 12 Pages |
Abstract
When the number of the experimental variables is large, the first and most critical step is to identify the (few) active factors among those (many) candidate factors. Supersaturated design is shown to be helpful for such a critical first step. A general construction method for mixed-level supersaturated design is proposed. The newly constructed design has several advantages, including the flexibility for the number of runs and the assurance of upper bound of the (pairwise) dependency among all design columns. Specific applications to the construction of two-level and three-level mixed-level designs are discussed in detail.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Shu Yamada, Michiyo Matsui, Tomomi Matsui, Dennis K.J. Lin, Takenori Takahashi,