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Registro Completo |
Biblioteca(s): |
Embrapa Unidades Centrais. |
Data corrente: |
18/01/2017 |
Data da última atualização: |
18/01/2017 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
SOUZA, G. da S. e; GOMES, E. G. |
Afiliação: |
GERALDO DA SILVA E SOUZA, SGI; ELIANE GONCALVES GOMES, SGI. |
Título: |
Management of agricultural research centers in Brazil: a DEA application using a dynamic GMM approach. |
Ano de publicação: |
2015 |
Fonte/Imprenta: |
European Journal of Operational Research, v. 240, p. 819-824, 2015. |
Idioma: |
Português |
Palavras-Chave: |
Agricultural research center; Data Envelopment Analysis model; DEA; Process production. |
Thesagro: |
Performance. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/153490/1/Management-agricultural-research.pdf
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Marc: |
LEADER 00608naa a2200181 a 4500 001 2061107 005 2017-01-18 008 2015 bl uuuu u00u1 u #d 100 1 $aSOUZA, G. da S. e 245 $aManagement of agricultural research centers in Brazil$ba DEA application using a dynamic GMM approach.$h[electronic resource] 260 $c2015 650 $aPerformance 653 $aAgricultural research center 653 $aData Envelopment Analysis model 653 $aDEA 653 $aProcess production 700 1 $aGOMES, E. G. 773 $tEuropean Journal of Operational Research$gv. 240, p. 819-824, 2015.
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Embrapa Unidades Centrais (AI-SEDE) |
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Registro Completo
Biblioteca(s): |
Embrapa Café. |
Data corrente: |
19/01/2022 |
Data da última atualização: |
19/01/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F.; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. |
Afiliação: |
JAQUICELE APARECIDA DA COSTA, UFV; CAMILA FERREIRA AZEVEDO, UFV; MOYSÉS NASCIMENTO, UFV; FABYANO FONSECA E SILVA, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; ANA CAROLINA CAMPANA NASCIMENTO, UFV. |
Título: |
Determination of optimal number of independent components in yield traits in rice. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
Scientia Agricola, v. 79, n. 6, p. 1-8, 2022. |
DOI: |
https://doi.org/10.1590/1678-992X-2020-0397 |
Idioma: |
Inglês |
Conteúdo: |
The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values. |
Thesagro: |
Arroz; Melhoramento Genético Vegetal; Produtividade. |
Thesaurus NAL: |
Genomics; Plant breeding; Rice; Yields. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230385/1/determination-of-optimal-number.pdf
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Marc: |
LEADER 01768naa a2200277 a 4500 001 2139185 005 2022-01-19 008 2022 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1590/1678-992X-2020-0397$2DOI 100 1 $aCOSTA, J. A. da 245 $aDetermination of optimal number of independent components in yield traits in rice.$h[electronic resource] 260 $c2022 520 $aThe principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values. 650 $aGenomics 650 $aPlant breeding 650 $aRice 650 $aYields 650 $aArroz 650 $aMelhoramento Genético Vegetal 650 $aProdutividade 700 1 $aAZEVEDO, C. F. 700 1 $aNASCIMENTO, M. 700 1 $aSILVA, F. F. 700 1 $aRESENDE, M. D. V. de 700 1 $aNASCIMENTO, A. C. C. 773 $tScientia Agricola$gv. 79, n. 6, p. 1-8, 2022.
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