A Method for Augmenting Supersaturated Designs with Newly Added Factors

Chun-Wei Zheng, Zong-Feng Qi, Qiao-Zhen Zhang and Min-Qian Liu ()
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Chun-Wei Zheng: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China
Zong-Feng Qi: State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE), Luoyang 471003, China
Qiao-Zhen Zhang: School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin 300071, China
Min-Qian Liu: School of Statistics and Data Science, LPMC & KLMDASR, Nankai University, Tianjin 300071, China

Mathematics, 2022, vol. 11, issue 1, 1-17

Abstract: Follow-up experimental designs are popularly used in industry. In practice, some important factors may be neglected for various reasons in the first-stage experiment and they need to be added in the next stage. In this paper, we propose a method for augmenting supersaturated designs with newly added factors and augmented levels using the Bayesian D -optimality criterion. In addition, we suggest using the integrated Bayesian D -optimal augmented design to plan the follow-up experiment when the newly added factors have been allowed to vary in an appropriate region. Examples and simulation results show that the augmented designs perform well in improving identified rates of latent factor effects.

Keywords: Bayesian design; augmented design; variable selection; newly added factors (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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