Improved Split-Plot and Multistratum Designs
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/00401706.2014.915235 http://hdl.handle.net/11449/160658 |
Resumo: | Many industrial experiments involve some factors whose levels are harder to set than others. The best way to deal with these is to plan the experiment carefully as a split-plot, or more generally a multistratum, design. Several different approaches for constructing split-plot type response surface designs have been proposed in the literature since 2001, which has allowed experimenters to make better use of their resources by using more efficient designs than the classical balanced ones. One of these approaches, the stratum-by-stratum strategy has been shown to produce designs that are less efficient than locally D-optimal designs. An improved stratum-by-stratum algorithm is given, which, though more computationally intensive than the old one, makes better use of the advantages of this approach, that is, it can be used for any structure and does not depend on prior estimates of the variance components. This is shown to be almost as good as the locally optimal designs in terms of their own criteria and more robust across a range of criteria. Supplementary materials for this article are available online. |
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Improved Split-Plot and Multistratum DesignsResponse surface.Hard-to-change factorD-optimalityPrediction varianceMixed modelHard-to-set factorA-optimalityMany industrial experiments involve some factors whose levels are harder to set than others. The best way to deal with these is to plan the experiment carefully as a split-plot, or more generally a multistratum, design. Several different approaches for constructing split-plot type response surface designs have been proposed in the literature since 2001, which has allowed experimenters to make better use of their resources by using more efficient designs than the classical balanced ones. One of these approaches, the stratum-by-stratum strategy has been shown to produce designs that are less efficient than locally D-optimal designs. An improved stratum-by-stratum algorithm is given, which, though more computationally intensive than the old one, makes better use of the advantages of this approach, that is, it can be used for any structure and does not depend on prior estimates of the variance components. This is shown to be almost as good as the locally optimal designs in terms of their own criteria and more robust across a range of criteria. Supplementary materials for this article are available online.EPSRCFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Engineering and Physical Sciences Research CouncilSao Paulo State Univ, Dept Biostat, Botucatu, SP, BrazilUniv Southampton, Southampton Stat Sci Res Inst, Southampton SO17 1BJ, Hants, EnglandSao Paulo State Univ, Dept Biostat, Botucatu, SP, BrazilEPSRC: EP/C541715/1FAPESP: 2010/0250-08Engineering and Physical Sciences Research Council: EP/C541715/1Amer Statistical AssocUniversidade Estadual Paulista (Unesp)Univ SouthamptonTrinca, Luzia A. [UNESP]Gilmour, Steven G.2018-11-26T16:16:11Z2018-11-26T16:16:11Z2015-04-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article145-154application/pdfhttp://dx.doi.org/10.1080/00401706.2014.915235Technometrics. Alexandria: Amer Statistical Assoc, v. 57, n. 2, p. 145-154, 2015.0040-1706http://hdl.handle.net/11449/16065810.1080/00401706.2014.915235WOS:000357940300001WOS:000357940300001.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengTechnometrics1,546info:eu-repo/semantics/openAccess2023-12-08T06:18:35Zoai:repositorio.unesp.br:11449/160658Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:45:36.329238Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Improved Split-Plot and Multistratum Designs |
title |
Improved Split-Plot and Multistratum Designs |
spellingShingle |
Improved Split-Plot and Multistratum Designs Trinca, Luzia A. [UNESP] Response surface. Hard-to-change factor D-optimality Prediction variance Mixed model Hard-to-set factor A-optimality |
title_short |
Improved Split-Plot and Multistratum Designs |
title_full |
Improved Split-Plot and Multistratum Designs |
title_fullStr |
Improved Split-Plot and Multistratum Designs |
title_full_unstemmed |
Improved Split-Plot and Multistratum Designs |
title_sort |
Improved Split-Plot and Multistratum Designs |
author |
Trinca, Luzia A. [UNESP] |
author_facet |
Trinca, Luzia A. [UNESP] Gilmour, Steven G. |
author_role |
author |
author2 |
Gilmour, Steven G. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Univ Southampton |
dc.contributor.author.fl_str_mv |
Trinca, Luzia A. [UNESP] Gilmour, Steven G. |
dc.subject.por.fl_str_mv |
Response surface. Hard-to-change factor D-optimality Prediction variance Mixed model Hard-to-set factor A-optimality |
topic |
Response surface. Hard-to-change factor D-optimality Prediction variance Mixed model Hard-to-set factor A-optimality |
description |
Many industrial experiments involve some factors whose levels are harder to set than others. The best way to deal with these is to plan the experiment carefully as a split-plot, or more generally a multistratum, design. Several different approaches for constructing split-plot type response surface designs have been proposed in the literature since 2001, which has allowed experimenters to make better use of their resources by using more efficient designs than the classical balanced ones. One of these approaches, the stratum-by-stratum strategy has been shown to produce designs that are less efficient than locally D-optimal designs. An improved stratum-by-stratum algorithm is given, which, though more computationally intensive than the old one, makes better use of the advantages of this approach, that is, it can be used for any structure and does not depend on prior estimates of the variance components. This is shown to be almost as good as the locally optimal designs in terms of their own criteria and more robust across a range of criteria. Supplementary materials for this article are available online. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-04-03 2018-11-26T16:16:11Z 2018-11-26T16:16:11Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1080/00401706.2014.915235 Technometrics. Alexandria: Amer Statistical Assoc, v. 57, n. 2, p. 145-154, 2015. 0040-1706 http://hdl.handle.net/11449/160658 10.1080/00401706.2014.915235 WOS:000357940300001 WOS:000357940300001.pdf |
url |
http://dx.doi.org/10.1080/00401706.2014.915235 http://hdl.handle.net/11449/160658 |
identifier_str_mv |
Technometrics. Alexandria: Amer Statistical Assoc, v. 57, n. 2, p. 145-154, 2015. 0040-1706 10.1080/00401706.2014.915235 WOS:000357940300001 WOS:000357940300001.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Technometrics 1,546 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
145-154 application/pdf |
dc.publisher.none.fl_str_mv |
Amer Statistical Assoc |
publisher.none.fl_str_mv |
Amer Statistical Assoc |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129114906820608 |