Some results on constructing three-level blocked designs with general minimum lower-order confounding

Zhi Li, Zhiming Li, Rui Tian and Zhengqi Li

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 22, 7105-7122

Abstract: Blocked designs are widely used in experimental situations when the experimental units are not homogeneous. This article introduces the blocked general minimum lower-order confounding (B1-GMC) criterion for selecting optimal three-level blocked designs. Some properties of three-level B1-GMC designs are provided in terms of their complementary sets. We obtain a systematic theory on constructing three-level B1-GMC designs. Several efficient algorithms for finding three-level B1-GMC designs are provided and implemented by Python. For application, B1-GMC designs with 27-, 81- and 243-run, respectively, are tabulated.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2025.2467196 (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:54:y:2025:i:22:p:7105-7122

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

DOI: 10.1080/03610926.2025.2467196

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 ().