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Registro Completo |
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Biblioteca(s): |
Embrapa Hortaliças; Embrapa Unidades Centrais. |
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Data corrente: |
03/03/1997 |
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Data da última atualização: |
12/12/2007 |
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Autoria: |
SILVA, J. G. C. da. |
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Afiliação: |
EMBRAPA/DMQ. |
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Título: |
The analysis of cross-sectional time series data. |
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Ano de publicação: |
1975 |
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Fonte/Imprenta: |
1975 . |
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Idioma: |
Inglês |
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Notas: |
Tese (Doutorado)- Graduate Faculty of Norts Carolina State University at Raleigh, Raleigh. |
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Conteúdo: |
This study is concerned with the estimation of linear relationships from cross-sectional time series data. The subject has been extensively discussed in the econometric literature. The diversity of the approaches proposed in the literature sistems both from the different sets of assumptions and the different estimation procedures adopted. Most of the alternative approahes are based on simpler assumptions than the more realistic assumptions used here. The variance component approaches ignore the possibility of serial correlation in the time direction. The seemingly unrelated regressions approaches assume a specific first order autoregressive error structure and treat cross-sectional unit effects as fixed rather than random. Two models are proposed to fit alternative situations. Model A assumes that the linear relationship is affected by a random disturbance with three... |
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Palavras-Chave: |
Analysis; Analysis statistic; Corte transversal; Cross section; Data; Econometric; Econometry; Estatistica experimental; Experimental statistic; Linear model; Mathematical model; Modelo linear; Serie de tempo; Série temporal; Time series; Tyme series analysis. |
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Thesagro: |
Análise Estatística; Dado; Econometria; Modelo Matemático. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 01882nam a2200361 a 4500
001 1099197
005 2007-12-12
008 1975 bl uuuu m 00u1 u #d
100 1 $aSILVA, J. G. C. da
245 $aThe analysis of cross-sectional time series data.
260 $a1975 .$c1975
500 $aTese (Doutorado)- Graduate Faculty of Norts Carolina State University at Raleigh, Raleigh.
520 $aThis study is concerned with the estimation of linear relationships from cross-sectional time series data. The subject has been extensively discussed in the econometric literature. The diversity of the approaches proposed in the literature sistems both from the different sets of assumptions and the different estimation procedures adopted. Most of the alternative approahes are based on simpler assumptions than the more realistic assumptions used here. The variance component approaches ignore the possibility of serial correlation in the time direction. The seemingly unrelated regressions approaches assume a specific first order autoregressive error structure and treat cross-sectional unit effects as fixed rather than random. Two models are proposed to fit alternative situations. Model A assumes that the linear relationship is affected by a random disturbance with three...
650 $aAnálise Estatística
650 $aDado
650 $aEconometria
650 $aModelo Matemático
653 $aAnalysis
653 $aAnalysis statistic
653 $aCorte transversal
653 $aCross section
653 $aData
653 $aEconometric
653 $aEconometry
653 $aEstatistica experimental
653 $aExperimental statistic
653 $aLinear model
653 $aMathematical model
653 $aModelo linear
653 $aSerie de tempo
653 $aSérie temporal
653 $aTime series
653 $aTyme series analysis
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
27/01/2021 |
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Data da última atualização: |
27/01/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
ALVES, R. S.; RESENDE, M. D. V. de; AZEVEDO, C. F.; SILVA, F. F. e; ROCHA, J. R. A. S. C.; NUNES, A. C. P.; CARNEIRO, A. P. S.; SANTOS, G. A. dos. |
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Afiliação: |
RODRIGO SILVA ALVES, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa; CAMILA FERREIRA AZEVEDO, UFV; FABYANO FONSECA E SILVA, UFV; JOÃO ROMERO DO AMARAL SANTOS DE CARVALHO ROCHA, UFV; ANDREI CAÍQUE PIRES NUNES, UFV; ANTÔNIO POLICARPO SOUZA CARNEIRO, UFV; GLEISON AUGUSTO DOS SANTOS, UFV. |
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Título: |
Optimization of Eucalyptus breeding through random regression models allowing for reaction norms in response to environmental gradients. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Tree Genetics & Genomes, v. 16, n. 2, p. 1-8, 2020. |
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DOI: |
https://doi.org/10.1007/s11295-020-01431-5 |
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Idioma: |
Inglês |
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Conteúdo: |
Reaction norms fitted through random regression models are a powerful tool to identify and quantify the genotype × environment (G × E) interaction and they represent a promising alternative in forest tree breeding for analysis of multi-environment trials. Thus, the objective of this study was to compare random regression models with the compound symmetry model in Eucalyptus breeding for analysis of multi-environment trials. To this end, a data set with 215 Eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The random regression models provided a better fit for both traits. Results showed that there was genotypic variability among Eucalyptus clones and that the reaction norms over the environmental gradients identified the G × E interaction. The compound symmetry model and the random regression models are highly correlated in terms of genotype ranking for both traits. The main advantage of random regression models over the compound symmetry model is the ability to predict genotypic performance in environments where a genotype has not been evaluated. Thus, our results suggest that reaction norms fitted through random regression models can be successfully used in forest tree breeding for analysis of multi-environment trials. |
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Thesagro: |
Árvore Florestal; Interação Genética; Seleção Genótipa. |
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Thesaurus NAL: |
Forest trees; Genotype-environment interaction; Plant selection guides. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02299naa a2200289 a 4500
001 2129599
005 2021-01-27
008 2020 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1007/s11295-020-01431-5$2DOI
100 1 $aALVES, R. S.
245 $aOptimization of Eucalyptus breeding through random regression models allowing for reaction norms in response to environmental gradients.$h[electronic resource]
260 $c2020
520 $aReaction norms fitted through random regression models are a powerful tool to identify and quantify the genotype × environment (G × E) interaction and they represent a promising alternative in forest tree breeding for analysis of multi-environment trials. Thus, the objective of this study was to compare random regression models with the compound symmetry model in Eucalyptus breeding for analysis of multi-environment trials. To this end, a data set with 215 Eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The random regression models provided a better fit for both traits. Results showed that there was genotypic variability among Eucalyptus clones and that the reaction norms over the environmental gradients identified the G × E interaction. The compound symmetry model and the random regression models are highly correlated in terms of genotype ranking for both traits. The main advantage of random regression models over the compound symmetry model is the ability to predict genotypic performance in environments where a genotype has not been evaluated. Thus, our results suggest that reaction norms fitted through random regression models can be successfully used in forest tree breeding for analysis of multi-environment trials.
650 $aForest trees
650 $aGenotype-environment interaction
650 $aPlant selection guides
650 $aÁrvore Florestal
650 $aInteração Genética
650 $aSeleção Genótipa
700 1 $aRESENDE, M. D. V. de
700 1 $aAZEVEDO, C. F.
700 1 $aSILVA, F. F. e
700 1 $aROCHA, J. R. A. S. C.
700 1 $aNUNES, A. C. P.
700 1 $aCARNEIRO, A. P. S.
700 1 $aSANTOS, G. A. dos
773 $tTree Genetics & Genomes$gv. 16, n. 2, p. 1-8, 2020.
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Registro original: |
Embrapa Café (CNPCa) |
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