Partial aliased effect number pattern and selection of optimal compromise designs

Shili Ye, Dongying Wang and Runchu Zhang ()
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Shili Ye: Southwest Forestry University
Dongying Wang: Jilin University of Finance and Economics
Runchu Zhang: Nankai University

Metrika: International Journal for Theoretical and Applied Statistics, 2019, vol. 82, issue 3, No 1, 269-293

Abstract: Abstract Often, experimenters are only interested in estimating a few factor specified effects. In this paper, we broadly call a design which can reach this target a compromise design. First, for assessing and selecting this kind of designs we introduce a partial aliased effect number pattern (P-AENP), then we use this pattern to study class one two-level compromise designs. Some theoretical results are obtained and a number of class one clear, strongly clear and general optimal $$2^{n-m}$$ 2 n - m compromise designs with 8, 16, 32 and 64 runs are tabulated.

Keywords: Aliased effect-number pattern; Clear effect; Compromise design; Fractional factorial design; General minimum lower order confounding (GMC); Primary 62K15; Secondary 62K05 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00184-018-00705-2

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