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A method for augmenting supersaturated designs
- Source :
- Journal of Statistical Planning and Inference. 199:207-218
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
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.
- Subjects :
- Statistics and Probability
Mathematical optimization
Optimality criterion
Applied Mathematics
05 social sciences
Bayesian probability
Estimator
Blocking factor
01 natural sciences
Two stages
010104 statistics & probability
0502 economics and business
Mean-shift
0101 mathematics
Statistics, Probability and Uncertainty
Focus (optics)
050205 econometrics
Mathematics
Subjects
Details
- ISSN :
- 03783758
- Volume :
- 199
- Database :
- OpenAIRE
- Journal :
- Journal of Statistical Planning and Inference
- Accession number :
- edsair.doi...........0ffec3e8e1a94399b926edfb29c04dc9