Orthogonal-array composite design for the third-order models

Xue-Ru Zhang, Zong-Feng Qi, Yong-Dao Zhou and Feng Yang

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 14, 3488-3507

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2017.1359297 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:47:y:2018:i:14:p:3488-3507

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2017.1359297

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().