Blocked factor aliased effect-number pattern and column rank of blocked regular designs

Dongying Wang, Shili Ye, Qi Zhou and Runchu Zhang ()
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Dongying Wang: Northeast Normal University
Shili Ye: Northeast Normal University
Qi Zhou: Tianjin University of Finance and Economics
Runchu Zhang: Northeast Normal University

Metrika: International Journal for Theoretical and Applied Statistics, 2017, vol. 80, issue 2, No 1, 133-152

Abstract: Abstract In factorial experiments, estimation precision of specific factor effects depends not only on design selection but also on factor assignments to columns of selected designs. Usually, different columns in a design play different roles when estimating factor effects. Zhou et al. (Can J Stat 41:540-555, 2013) introduced a factor aliased effect-number pattern (F-AENP) and proposed a column ranking scheme for all the GMC $$2^{n-m}$$ 2 n - m designs with $$5N/16+1\le n\le N-1$$ 5 N / 16 + 1 ≤ n ≤ N - 1 , where $$N=2^{n-m}$$ N = 2 n - m . In this paper, we first introduce a blocked factor aliased effect-number pattern (B-F-AENP) for blocked regular designs as an extension of the F-AENP. Then, by using the B-F-AENP, we propose a column ranking scheme for all the B $$^1$$ 1 -GMC $$2^{n-m}:2^s$$ 2 n - m : 2 s designs with $$5N/16+1\le n\le N-1$$ 5 N / 16 + 1 ≤ n ≤ N - 1 , as well as an assignment strategy for important factors.

Keywords: Aliased effect-number pattern; Blocked; Factor aliased effect-number pattern; Fractional factorial design; General minimum lower order confounding (GMC); Primary 62K15; Secondary 62K05 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00184-016-0595-7

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