Large supersaturated designs

Eskridge, K. M., Gilmour, S. G., Mead, R., Butler, N. A. and Travnicek, D. A. (2004) Large supersaturated designs. Journal of Statistical Computation and Simulation, 74 (7). pp. 525-542. ISSN 0094-9655 doi: 10.1080/00949650310001612436

Abstract/Summary

A supersaturated design (SSD) is an experimental plan, useful for evaluating the main effects of m factors with n experimental units when m > n - 1, each factor has two levels and when the first-order effects of only a few factors are expected to have dominant effects on the response. Use of these plans can be extremely cost-effective when it is necessary to screen hundreds or thousands of factors with a limited amount of resources. In this article we describe how to use cyclic balanced incomplete block designs and regular graph designs to construct E (s(2)) optimal and near optimal SSDs when m is a multiple of n - 1. We also provide a table that can be used to construct these designs for screening thousands of factors. We also explain how to obtain SSDs when m is not a multiple of n - 1. Using the table and the approaches given in this paper, SSDs can be developed for designs with up to 24 runs and up to 12,190 factors.