 Choice of optimal second stage designs in two-stage experimentsA. Elsawah () Computational Statistics, 2018, vol. 33, issue 2, No 16, 933-965 Abstract: Abstract In real-life projects, in order to obtain precious information about the process, we often partition the experiment into two stages with equal size. The main purpose of this article is to study how to choose the first stage experimental designs (FSED) and the second stage experimental designs (SSED) to construct uniform or at least good approximation to uniform (GATU) two-stage experimental designs (TSED) that involve a mixture of $$\omega _1\ge 1$$ ω 1 ≥ 1 factors with $$\mu _1\ge 2$$ μ 1 ≥ 2 levels and $$\omega _2\ge 1$$ ω 2 ≥ 1 factors with $$\mu _2\ge 2$$ μ 2 ≥ 2 levels whether regular or nonregular. Through theoretical justification, this paper proves that the SSED is uniform (GATU) if and only if the FSED is uniform (GATU), the TSED is uniform (GATU) if and only if its corresponding complementary TSED is uniform (GATU), and the TSED is uniform or at least GATU if and only if the FSED is uniform. Keywords: Second stage design; Second stage map; Two-stage design; Uniform design; Optimal design; Complementary design (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:33:y:2018:i:2:d:10.1007_s00180-017-0778-3 Ordering information: This journal article can be ordered from DOI: 10.1007/s00180-017-0778-3 Access Statistics for this article Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik More articles in Computational Statistics from Springer |
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