JSM 2004 Online Program

Abstract #300293

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Activity Number:	421
Type:	Invited
Date/Time:	Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor:	Section on Quality and Productivity
Abstract - #300293
Title:	Bayesian D-optimal Supersaturated Design
Author(s):	Bradley Jones*+
Companies:	SAS Institute Inc.
Address:	, Cary, NC, 27513,
Keywords:	optimal design ; supersaturated ; Bayesian
Abstract:	This talk introduces a new class of supersaturated designs using Bayesian D-optimality. The design 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(s^2) criterion for two-level experiments having even sample size, these designs either match or out-perform the best designs published to date. To show the practical benefit of the new method, we present a case study design having 15 runs in 20 factors divided into three blocks of size five.

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