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A method for augmenting supersaturated designs

Zhang, Qiao-Zhen and Dai, Hongsheng and Liu, Min-Qian and Wang, Ya (2018) 'A method for augmenting supersaturated designs.' Journal of Statistical Planning and Inference. ISSN 0378-3758

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Abstract

Initial screening experiments often leave some problems unresolved, adding follow-up runs is needed to clarify the initial results. In this paper, a technique is developed to add additional experimental runs to an initial supersaturated design. The added runs are generated with respect to the Bayesian $D_s$-optimality criterion and the procedure can incorporate the model information from the initial design. After analysis of the initial experiment with several methods, factors are classified into three groups: primary, secondary, and potential according to the times that they have been identified. The focus is on those secondary factors since they have been identified several times but not so many that experimenters are sure that they are active, the proposed Bayesian $D_s$-optimal augmented design would minimize the error variances of the parameter estimators of secondary factors. In addition, a blocking factor will be involved to describe the mean shift between two stages. Simulation results show that the method performs very well in certain settings.

Item Type: Article
Uncontrolled Keywords: Bayesian D-optimality, Coordinate-exchange algorithm, Follow-up experiment, Sequential design, Supersaturated design
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Elements
Date Deposited: 27 Jun 2018 11:56
Last Modified: 03 Jul 2019 01:00
URI: http://repository.essex.ac.uk/id/eprint/22328

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