Selecting baseline designs using a minimum aberration criterion when some two-factor interactions are important

Anqi Chen, Cheng-Yu Sun and Boxin Tang

Statistical Theory and Related Fields, 2021, vol. 5, issue 2, 95-101

Abstract: This article considers the problem of selecting two-level designs under the baseline parameterisation when some two-factor interactions are important. We propose a minimum aberration criterion, which minimises the bias caused by the non-negligible effects. Using this criterion, a class of optimal designs can be further distinguished from one another, and we present an algorithm to find the minimum aberration designs among the D-optimal designs. Sixteen-run and twenty-run designs are summarised for practical use.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2020.1867795 (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:tstfxx:v:5:y:2021:i:2:p:95-101

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20

DOI: 10.1080/24754269.2020.1867795

Access Statistics for this article

Statistical Theory and Related Fields is currently edited by Zhao Wei

More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().