Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.

Detalhes bibliográficos
Autor(a) principal: GOMES, E. G.
Data de Publicação: 2008
Outros Autores: SOUZA, G. da S. e, VIVALDI, L. J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Texto Completo: http://www.alice.cnptia.embrapa.br/alice/handle/doc/657613
Resumo: In this article we propose the use of Data Envelopment Analysis (DEA) measures of efficiency, under constant returns to scale and input equal to unity, in the analysis of multidimensional nonnegative responses in the design of experiments. The approach agrees with the standard Analysis of Variance (Covariance) for univariate responses and simplifies the statistical analysis in the multivariate case. The best treatments provided by the analysis optimize a combined output defined by shadow prices, which are the solutions of the DEA problem. The approach is particularly useful for the analysis of intercropping (crop mixtures) experiments. In this context we discuss two examples. To properly address the issue of correlation and non-normality of DEA measurements in different experimental plots we validate the results via Randomization Theory.
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spelling Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.Ensaio experimentalConsórcioAnálise envoltória de dadosData envelopment analysisexperimental designintercroppingIn this article we propose the use of Data Envelopment Analysis (DEA) measures of efficiency, under constant returns to scale and input equal to unity, in the analysis of multidimensional nonnegative responses in the design of experiments. The approach agrees with the standard Analysis of Variance (Covariance) for univariate responses and simplifies the statistical analysis in the multivariate case. The best treatments provided by the analysis optimize a combined output defined by shadow prices, which are the solutions of the DEA problem. The approach is particularly useful for the analysis of intercropping (crop mixtures) experiments. In this context we discuss two examples. To properly address the issue of correlation and non-normality of DEA measurements in different experimental plots we validate the results via Randomization Theory.Eliane Gonçalves Gomes, Embrapa-SGE; GERALDO DA SILVA E SOUZA, Embrapa-SGE; Lúcio José Vivaldi, Universidade de Brasília.GOMES, E. G.SOUZA, G. da S. eVIVALDI, L. J.2011-04-09T12:48:28Z2011-04-09T12:48:28Z2010-02-1020082011-04-10T11:11:11Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlePesquisa Operacional, v.28, n.2, p. 339-354, mai./ago. 2008.http://www.alice.cnptia.embrapa.br/alice/handle/doc/657613enginfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)instacron:EMBRAPA2017-08-16T01:14:53Zoai:www.alice.cnptia.embrapa.br:doc/657613Repositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestopendoar:21542017-08-16T01:14:53falseRepositório InstitucionalPUBhttps://www.alice.cnptia.embrapa.br/oai/requestcg-riaa@embrapa.bropendoar:21542017-08-16T01:14:53Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)false
dc.title.none.fl_str_mv Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.
title Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.
spellingShingle Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.
GOMES, E. G.
Ensaio experimental
Consórcio
Análise envoltória de dados
Data envelopment analysis
experimental design
intercropping
title_short Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.
title_full Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.
title_fullStr Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.
title_full_unstemmed Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.
title_sort Two-stage inference in experimental design using DEA: an application to intercropping and evidence from randomization theory.
author GOMES, E. G.
author_facet GOMES, E. G.
SOUZA, G. da S. e
VIVALDI, L. J.
author_role author
author2 SOUZA, G. da S. e
VIVALDI, L. J.
author2_role author
author
dc.contributor.none.fl_str_mv Eliane Gonçalves Gomes, Embrapa-SGE; GERALDO DA SILVA E SOUZA, Embrapa-SGE; Lúcio José Vivaldi, Universidade de Brasília.
dc.contributor.author.fl_str_mv GOMES, E. G.
SOUZA, G. da S. e
VIVALDI, L. J.
dc.subject.por.fl_str_mv Ensaio experimental
Consórcio
Análise envoltória de dados
Data envelopment analysis
experimental design
intercropping
topic Ensaio experimental
Consórcio
Análise envoltória de dados
Data envelopment analysis
experimental design
intercropping
description In this article we propose the use of Data Envelopment Analysis (DEA) measures of efficiency, under constant returns to scale and input equal to unity, in the analysis of multidimensional nonnegative responses in the design of experiments. The approach agrees with the standard Analysis of Variance (Covariance) for univariate responses and simplifies the statistical analysis in the multivariate case. The best treatments provided by the analysis optimize a combined output defined by shadow prices, which are the solutions of the DEA problem. The approach is particularly useful for the analysis of intercropping (crop mixtures) experiments. In this context we discuss two examples. To properly address the issue of correlation and non-normality of DEA measurements in different experimental plots we validate the results via Randomization Theory.
publishDate 2008
dc.date.none.fl_str_mv 2008
2010-02-10
2011-04-09T12:48:28Z
2011-04-09T12:48:28Z
2011-04-10T11:11:11Z
dc.type.driver.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv Pesquisa Operacional, v.28, n.2, p. 339-354, mai./ago. 2008.
http://www.alice.cnptia.embrapa.br/alice/handle/doc/657613
identifier_str_mv Pesquisa Operacional, v.28, n.2, p. 339-354, mai./ago. 2008.
url http://www.alice.cnptia.embrapa.br/alice/handle/doc/657613
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
instname:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron:EMBRAPA
instname_str Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
instacron_str EMBRAPA
institution EMBRAPA
reponame_str Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
collection Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
repository.name.fl_str_mv Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice) - Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
repository.mail.fl_str_mv cg-riaa@embrapa.br
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