On simplifying the calculations leading to designs with general minimum lower-order confounding

Jia-Lin Wei and Jian-Feng Yang ()

Metrika: International Journal for Theoretical and Applied Statistics, 2013, vol. 76, issue 5, 723-732

Abstract: Motivated by the effect hierarchy principle, Zhang et al. (Stat Sinica 18:1689–1705, 2008 ) introduced an aliased effect number pattern (AENP) for regular fractional factorial designs and based on the new pattern proposed a general minimum lower-order confounding (GMC) criterion for choosing optimal $$2^{n-m}$$ designs. Zhang et al. (Stat Sinica 18:1689–1705, 2008 ) proved that most existing criteria can be obtained by functions of the AENP. In this paper we propose a simple method for the calculation of AENP. The method is much easier than before since the calculation only makes use of the design matrix. All 128-run GMC designs with the number of factors ranging from 8 to 32 are provided for practical use. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Aliased effect-number pattern; Clear; Fractional factorial design; GMC; Minimum aberration (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s00184-013-0442-z (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:spr:metrik:v:76:y:2013:i:5:p:723-732

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s00184-013-0442-z

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().