Split-Plot and Multi-Stratum Designs for Statistical Inference
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1080/00401706.2017.1316315 http://hdl.handle.net/11449/163666 |
Resumo: | It is increasingly recognized that many industrial and engineering experiments use split-plot or other multi-stratum structures. Much recent work has concentrated on finding optimum, or near-optimum, designs for estimating the fixed effects parameters in multi-stratum designs. However, often inference, such as hypothesis testing or interval estimation, will also be required and for inference to be unbiased in the presence of model uncertainty requires pure error estimates of the variance components. Most optimal designs provide few, if any, pure error degrees of freedom. Gilmour and Trinca (2012) introduced design optimality criteria for inference in the context of completely randomized and block designs. Here these criteria are used stratum-by-stratum to obtain multi-stratum designs. It is shown that these designs have better properties for performing inference than standard optimum designs. Compound criteria, which combine the inference criteria with traditional point estimation criteria, are also used and the designs obtained are shown to compromise between point estimation and inference. Designs are obtained for two real split-plot experiments and an illustrative split-split-plot structure. Supplementary materials for this article are available online. |
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Split-Plot and Multi-Stratum Designs for Statistical InferenceA-optimalityD-optimalityHard-to-change factorHard-to-set factorMixed modelResponse surfaceSplit-split-plot designIt is increasingly recognized that many industrial and engineering experiments use split-plot or other multi-stratum structures. Much recent work has concentrated on finding optimum, or near-optimum, designs for estimating the fixed effects parameters in multi-stratum designs. However, often inference, such as hypothesis testing or interval estimation, will also be required and for inference to be unbiased in the presence of model uncertainty requires pure error estimates of the variance components. Most optimal designs provide few, if any, pure error degrees of freedom. Gilmour and Trinca (2012) introduced design optimality criteria for inference in the context of completely randomized and block designs. Here these criteria are used stratum-by-stratum to obtain multi-stratum designs. It is shown that these designs have better properties for performing inference than standard optimum designs. Compound criteria, which combine the inference criteria with traditional point estimation criteria, are also used and the designs obtained are shown to compromise between point estimation and inference. Designs are obtained for two real split-plot experiments and an illustrative split-split-plot structure. Supplementary materials for this article are available online.UNESPFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Sao Paulo State Univ, Dept Biostat, Botucatu, SP, BrazilKings Coll London, Dept Math, London, EnglandSao Paulo State Univ, Dept Biostat, Botucatu, SP, BrazilUNESP: PDI/028900413/PROPG-CDCUNESP: PDI/828900413/PROPG-CDCFAPESP: 2014/01818-0Amer Statistical AssocUniversidade Estadual Paulista (Unesp)Kings Coll LondonTrinca, Luzia A. [UNESP]Gilmour, Steven G.2018-11-26T17:44:29Z2018-11-26T17:44:29Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article446-457application/pdfhttp://dx.doi.org/10.1080/00401706.2017.1316315Technometrics. Alexandria: Amer Statistical Assoc, v. 59, n. 4, p. 446-457, 2017.0040-1706http://hdl.handle.net/11449/16366610.1080/00401706.2017.1316315WOS:000418769600005WOS000418769600005.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengTechnometrics1,546info:eu-repo/semantics/openAccess2023-11-05T06:14:53Zoai:repositorio.unesp.br:11449/163666Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:00:05.324425Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Split-Plot and Multi-Stratum Designs for Statistical Inference |
title |
Split-Plot and Multi-Stratum Designs for Statistical Inference |
spellingShingle |
Split-Plot and Multi-Stratum Designs for Statistical Inference Trinca, Luzia A. [UNESP] A-optimality D-optimality Hard-to-change factor Hard-to-set factor Mixed model Response surface Split-split-plot design |
title_short |
Split-Plot and Multi-Stratum Designs for Statistical Inference |
title_full |
Split-Plot and Multi-Stratum Designs for Statistical Inference |
title_fullStr |
Split-Plot and Multi-Stratum Designs for Statistical Inference |
title_full_unstemmed |
Split-Plot and Multi-Stratum Designs for Statistical Inference |
title_sort |
Split-Plot and Multi-Stratum Designs for Statistical Inference |
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) Kings Coll London |
dc.contributor.author.fl_str_mv |
Trinca, Luzia A. [UNESP] Gilmour, Steven G. |
dc.subject.por.fl_str_mv |
A-optimality D-optimality Hard-to-change factor Hard-to-set factor Mixed model Response surface Split-split-plot design |
topic |
A-optimality D-optimality Hard-to-change factor Hard-to-set factor Mixed model Response surface Split-split-plot design |
description |
It is increasingly recognized that many industrial and engineering experiments use split-plot or other multi-stratum structures. Much recent work has concentrated on finding optimum, or near-optimum, designs for estimating the fixed effects parameters in multi-stratum designs. However, often inference, such as hypothesis testing or interval estimation, will also be required and for inference to be unbiased in the presence of model uncertainty requires pure error estimates of the variance components. Most optimal designs provide few, if any, pure error degrees of freedom. Gilmour and Trinca (2012) introduced design optimality criteria for inference in the context of completely randomized and block designs. Here these criteria are used stratum-by-stratum to obtain multi-stratum designs. It is shown that these designs have better properties for performing inference than standard optimum designs. Compound criteria, which combine the inference criteria with traditional point estimation criteria, are also used and the designs obtained are shown to compromise between point estimation and inference. Designs are obtained for two real split-plot experiments and an illustrative split-split-plot structure. Supplementary materials for this article are available online. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-11-26T17:44:29Z 2018-11-26T17:44:29Z |
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/10.1080/00401706.2017.1316315 Technometrics. Alexandria: Amer Statistical Assoc, v. 59, n. 4, p. 446-457, 2017. 0040-1706 http://hdl.handle.net/11449/163666 10.1080/00401706.2017.1316315 WOS:000418769600005 WOS000418769600005.pdf |
url |
http://dx.doi.org/10.1080/00401706.2017.1316315 http://hdl.handle.net/11449/163666 |
identifier_str_mv |
Technometrics. Alexandria: Amer Statistical Assoc, v. 59, n. 4, p. 446-457, 2017. 0040-1706 10.1080/00401706.2017.1316315 WOS:000418769600005 WOS000418769600005.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 |
446-457 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_ |
1808128735436603392 |