2004/08/01 by Dursun A. Bulutoglu, Ching-Shui Cheng
Computer Science · Decision Sciences · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Optimal Experimental Design Methods #Statistical Methods in Clinical Trials #math.ST #msc:62K10 #msc:62K15 #stat.TH
paper · pdf · doi:10.1214/009053604000000472
published as Annals of Statistics 2004, Vol. 32, No. 4, 1662-1678 · Published by the Institute of Mathematical Statistics (http://www.imstat.org) in the Annals of Statistics (http://www.imstat.org/aos/) at http://dx.doi.org/10.1214/009053604000000472
openalex publication_date 2004/08/01 · arxiv created 2004/10/05 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Booth and Cox proposed the E(s2) criterion for constructing two-level supersaturated designs. Nguyen [Technometrics 38 (1996) 69–73] and Tang and Wu [Canad. J. Statist 25 (1997) 191–201] independently derived a lower bound for E(s2). This lower bound can be achieved only when m is a multiple of N−1, where m is the number of factors and N is the run size. We present a method that uses difference families to construct designs that satisfy this lower bound. We also derive better lower bounds for the case where the Nguyen–Tang–Wu bound is not achievable. Our bounds cover more cases than a bound recently obtained by Butler, Mead, Eskridge and Gilmour [J. R. Stat. Soc. Ser. B Stat. Methodol. 63 (2001) 621–632]. New E(s2)-optimal designs are obtained by using a computer to search for designs that achieve the improved bounds.