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Three-level main-effects designs exploiting prior information about model uncertainty

Tsai, P. W., Gilmour, S. G. and Mead, R. (2007) Three-level main-effects designs exploiting prior information about model uncertainty. Journal of Statistical Planning and Inference, 137 (2). pp. 619-627. ISSN 0378-3758

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To link to this item DOI: 10.1016/j.jspi.2006.01.003

Abstract/Summary

To explore the projection efficiency of a design, Tsai, et al [2000. Projective three-level main effects designs robust to model uncertainty. Biometrika 87, 467-475] introduced the Q criterion to compare three-level main-effects designs for quantitative factors that allow the consideration of interactions in addition to main effects. In this paper, we extend their method and focus on the case in which experimenters have some prior knowledge, in advance of running the experiment, about the probabilities of effects being non-negligible. A criterion which incorporates experimenters' prior beliefs about the importance of each effect is introduced to compare orthogonal, or nearly orthogonal, main effects designs with robustness to interactions as a secondary consideration. We show that this criterion, exploiting prior information about model uncertainty, can lead to more appropriate designs reflecting experimenters' prior beliefs. (c) 2006 Elsevier B.V. All rights reserved.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences
ID Code:10100
Uncontrolled Keywords:Bayesian optimal designs, factor screening, orthogonal array, prior, information, projection, BAYESIAN VARIABLE-SELECTION, ROBUST

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