A Method for Augmenting Supersaturated Designs with Newly Added Factors

Autor:	Chun-Wei Zheng, Zong-Feng Qi, Qiao-Zhen Zhang, Min-Qian Liu
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Bayesian design augmented design variable selection newly added factors Mathematics QA1-939
Zdroj:	Mathematics, Vol 11, Iss 1, p 60 (2022)
Druh dokumentu:	article
ISSN:	2227-7390
DOI:	10.3390/math11010060
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/42f82927e42f443fa3f4658bdf2acb4f Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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