2018
DOI: 10.1111/rssb.12305
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Construction of Row–Column Factorial Designs

Abstract: Summary The arrangement of 2n‐factorials in row–column designs to estimate main effects and two‐factor interactions is investigated. Single‐replicate constructions are given which enable estimation of all main effects and maximize the number of estimable two‐factor interactions. Constructions and guidance are given for multireplicate designs in single arrays and in multiple arrays. Consideration is given to constructions for 2n−t fractional factorials.

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Cited by 11 publications
(14 citation statements)
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“…A similar result is given in Godolphin (2019) in relation to factorial designs arranged in a rectangular array. Theorem 2 prompts: Corollary 3 If n ≤ 2 q − 1 then all interactions in a blocked 2 n factorial are estimable, provided that X is formed from n distinct members of X q and has rank q, over GF(2).…”
Section: Preliminariessupporting
confidence: 71%
“…A similar result is given in Godolphin (2019) in relation to factorial designs arranged in a rectangular array. Theorem 2 prompts: Corollary 3 If n ≤ 2 q − 1 then all interactions in a blocked 2 n factorial are estimable, provided that X is formed from n distinct members of X q and has rank q, over GF(2).…”
Section: Preliminariessupporting
confidence: 71%
“…A result which is similar to Theorem 2 is given in Godolphin (2019) in relation to factorial designs arranged in a rectangular array. Theorem 2 prompts:…”
Section: Blocked Full Factorial Designssupporting
confidence: 66%
“…Blocking factors for factorial designs have been well-studied ( [1], [2], [10], [9], [17]). However, as mentioned in [13], having two forms of blocking for a factorial design is less well-studied.…”
Section: Introductionmentioning
confidence: 99%
“…Within experimental design, a row-column design can refer to a variety of combinatorial designs, all with the property of being arranged in a rectangular array, where the rows and columns are typically (but not always) blocking factors. This is sometimes referred to as double confounding [13]. To ensure that certain effects can be estimated without confounding, regularity conditions are imposed.…”
Section: Introductionmentioning
confidence: 99%
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