Orthogonal-array composite design for the third-order models

In many industrial trials, the second-order models may not be enough to fit the non linearity of the underlying model, and the third-order models may be considered. In this article, the orthogonal-array composite design (OACD), combined with two-level OA and four-level OA and denoted by OACD4, is pr...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2018-07, Vol.47 (14), p.3488-3507
Main Authors: Zhang, Xue-Ru, Qi, Zong-Feng, Zhou, Yong-Dao, Yang, Feng
Format: Article
Language:English
Subjects:
Chemical synthesis
Orthogonal arrays
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Summary:In many industrial trials, the second-order models may not be enough to fit the non linearity of the underlying model, and the third-order models may be considered. In this article, the orthogonal-array composite design (OACD), combined with two-level OA and four-level OA and denoted by OACD4, is proposed to estimate the second-order and third-order models. It is shown that OACD4 has good properties and has higher efficiency than other types of designs for the third-order models, and OACD4 can perform multiple analysis for cross-validation. The usefulness of OACD4 is also shown by a case study for polymer synthesis experiment.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2017.1359297