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| Registros recuperados : 317 | |
| 141. |  | PIRES, J. L. F.; COSTA, J. A.; THOMAS, A. L.; MAEHLER, A. R. Efeito de populações e espaçamentos sobre o potencial de rendimento da soja durante a ontogenia. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 35, n. 8, p. 1541-47, ago. 2000. Título em inglês: Effect of population and spacing on soybean potential yield during ontogeny.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 145. | | ALMEIDA, R. G. de; KICHEL, A. N.; COSTA, J. A. A. da; ZIMMER, A. H. Produtividade de grãos e de forragem em cultivo simultâneo de milho e capim-piatã na safrinha, com diferentes níveis de supressão do capim por herbicida¹. In: REUNIÃO ANUAL DA SOCIEDADE BRASILEIRA DE ZOOTECNIA, 48., 2011, Belém, PA. O desenvolvimento da produção animal e a responsabilidade frente a novos desafios: anais. Belém, PA: SBZ, 2011. 1 CD-ROM. SBZ 2011. 1-3 1 CD-ROM SBZ 2011| Biblioteca(s): Embrapa Gado de Corte. |
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| 149. | | COSTA, J. A. A. da; KICHEL, A. N.; STIEVEN, I.; ALMEIDA, R. G. de. Productivity and nutritive value of tropical forage intercropping with off-season corn. In:SIMPÓSIO INTERNACIONAL SOBRE MELHORAMENTO DE FORRAGEIRAS, 2., 2009, Campo Grande, MS. [Anais]... Campo Grande, MS: Embrapa Gado de Corte, 2009. II SIMF. Comitê editorial: Liana Jank; Lucimara Chiari; Rosângela Maria Simeão Resende. 4 p. 1CD-ROM. Resumo M 12.| Biblioteca(s): Embrapa Gado de Corte. |
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| 160. | | PIRES, J. L. F.; COSTA, J. A.; RAMBO, L.; FERREIRA, F. G. Métodos para a estimativa do potencial de rendimento da soja durante a ontogenia. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 40, n. 4, p. 337-344, abr. 2005 Título em inglês: Methods for estimating the soybean potential yield during ontogeny.| Biblioteca(s): Embrapa Unidades Centrais. |
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| Registros recuperados : 317 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
21/01/2022 |
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Data da última atualização: |
21/01/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
COSTA, J. A. da; AZEVEDO, C. F.; NASCIMENTO, M.; SILVA, F. F. e; RESENDE, M. D. V. de; NASCIMENTO, A. C. C. |
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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. |
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Título: |
A comparison of regression methods based on dimensional reduction for genomic prediction. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Genetics and Molecular Research, v. 20, n. 2, p. 1-15, 2021. |
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DOI: |
https://doi.org/10.4238/gmr18877 |
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Idioma: |
Inglês |
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Conteúdo: |
multicollinearity and high dimensionality problems, making it impossible to obtain stable estimates through the traditional method of estimation based on ordinary least squares. To overcome such challenges, dimensionality reduction methods have been proposed, because of their simple theory and easy application. We compared three dimensionality reduction methods: Principal Components Regression (PCR), Partial Least Squares (PLS), and Independent Components Regression (ICR). An important step for dimensionality reduction and prediction is selecting the number of components, as it affects the linear combinations of the explanatory variables. The linear combinations are inserted into the model to predict the response based on a reduced number of parameters. We examined the criteria for the selection of the number of components. The dimensionality reduction methods were applied to genomic and phenotype data. We evaluated 370 accessions of Asian rice, Oryza sativa, which were genotyped for 36,901 SNPs markers considered to predict the genomic values for the number of panicles per plant trait.This data set presented multicollinearity and high dimensionality. The computational time for each method was also recorded. Among the methods, PCR and ICR gave the highest accuracy values, with ICR standing out for presenting estimates of the least biased genomic values. However, ICR required more computational time than the other methodologies. |
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Thesaurus NAL: |
Genomics; Regression analysis. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/230432/1/A-comparison-of-regression-methods.pdf
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Marc: |
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024 7 $ahttps://doi.org/10.4238/gmr18877$2DOI
100 1 $aCOSTA, J. A. da
245 $aA comparison of regression methods based on dimensional reduction for genomic prediction.$h[electronic resource]
260 $c2021
520 $amulticollinearity and high dimensionality problems, making it impossible to obtain stable estimates through the traditional method of estimation based on ordinary least squares. To overcome such challenges, dimensionality reduction methods have been proposed, because of their simple theory and easy application. We compared three dimensionality reduction methods: Principal Components Regression (PCR), Partial Least Squares (PLS), and Independent Components Regression (ICR). An important step for dimensionality reduction and prediction is selecting the number of components, as it affects the linear combinations of the explanatory variables. The linear combinations are inserted into the model to predict the response based on a reduced number of parameters. We examined the criteria for the selection of the number of components. The dimensionality reduction methods were applied to genomic and phenotype data. We evaluated 370 accessions of Asian rice, Oryza sativa, which were genotyped for 36,901 SNPs markers considered to predict the genomic values for the number of panicles per plant trait.This data set presented multicollinearity and high dimensionality. The computational time for each method was also recorded. Among the methods, PCR and ICR gave the highest accuracy values, with ICR standing out for presenting estimates of the least biased genomic values. However, ICR required more computational time than the other methodologies.
650 $aGenomics
650 $aRegression analysis
700 1 $aAZEVEDO, C. F.
700 1 $aNASCIMENTO, M.
700 1 $aSILVA, F. F. e
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, A. C. C.
773 $tGenetics and Molecular Research$gv. 20, n. 2, p. 1-15, 2021.
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