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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
07/01/2015 |
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Data da última atualização: |
08/01/2015 |
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Tipo da produção científica: |
Capítulo em Livro Técnico-Científico |
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Autoria: |
CARVALHO, B. de S.; ARBEX, W. A. |
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Afiliação: |
BENILTON DE SÁ CARVALHO, UNICAMP; WAGNER ANTONIO ARBEX, CNPGL. |
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Título: |
Desafios e perspectivas da bioinformática. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
In: ARBEX, A.; MARTINS, N. F.; MARTINS, M. F. Talking about computing and genomics - TACG vol.1: Modelos e métodos computacionais em Bioinformática. Brasília, DF: Embrapa, 2014. |
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Páginas: |
p. 19-40 |
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Idioma: |
Português |
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Palavras-Chave: |
Estatística na bioinformática; Formação de pessoal; Multidisciplinaridade na bioinformática. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 00632naa a2200169 a 4500
001 2004666
005 2015-01-08
008 2014 bl uuuu u00u1 u #d
100 1 $aCARVALHO, B. de S.
245 $aDesafios e perspectivas da bioinformática.
260 $c2014
300 $ap. 19-40
653 $aEstatística na bioinformática
653 $aFormação de pessoal
653 $aMultidisciplinaridade na bioinformática
700 1 $aARBEX, W. A.
773 $tIn: ARBEX, A.; MARTINS, N. F.; MARTINS, M. F. Talking about computing and genomics - TACG vol.1: Modelos e métodos computacionais em Bioinformática. Brasília, DF: Embrapa, 2014.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
28/01/2021 |
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Data da última atualização: |
02/07/2021 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
C - 0 |
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Autoria: |
RESENDE, M. D. V. de; ALVES, R. S. |
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Afiliação: |
MARCOS DEON VILELA DE RESENDE, CNPCa; RODRIGO SILVA ALVES, UFV. |
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Título: |
Linear, generalized, hierarchical, bayesian and random regression mixed models in genetics/genomics in plant breeding. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
Functional Plant Breeding Journal, v. 2, n. 2, jul./dez., 2020. p. 1-31. |
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DOI: |
http://dx.doi.org/10.35418/2526-4117/v2n2a1 |
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Idioma: |
Inglês |
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Conteúdo: |
This paper presents the state of the art of the statistical modelling as applied to plant breeding. Classes of inference, statistical models, estimation methods and model selection are emphasized in a practical way. Restricted Maximum Likelihood (REML), Hierarchical Maximum Likelihood (HIML) and Bayesian (BAYES) are highlighted. Distributions of data and effects, and dimension and structure of the models are considered for model selection and parameters estimation. Theory and practical examples referring to selection between models with different fixed effects factors are given using the Full Maximum Likelihood (FML). An analytical FML way of defining random or fixed effects is presented to avoid the subjective or conceptual usual definitions. Examples of the applications of the Hierarchical Maximum Likelihood/Hierarchical Generalized Best Linear Unbiased Prediction (HIML/HG-BLUP) procedure are also presented. Sample sizes for achieving high experimental quality and accuracy are indicated and simple interpretation of the estimates of key genetic parameters are given. Phenomics and genomics are approached. Maximum accuracy under the truest model is the key for achieving efficacy in plant breeding programs. |
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Thesagro: |
Melhoramento Genético Vegetal; Método Estatístico. |
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Thesaurus NAL: |
Plant breeding; Statistical models. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/220720/1/Linear-generalized-hierarchical.pdf
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Marc: |
LEADER 01912naa a2200193 a 4500
001 2129637
005 2021-07-02
008 2020 bl uuuu u00u1 u #d
024 7 $ahttp://dx.doi.org/10.35418/2526-4117/v2n2a1$2DOI
100 1 $aRESENDE, M. D. V. de
245 $aLinear, generalized, hierarchical, bayesian and random regression mixed models in genetics/genomics in plant breeding.$h[electronic resource]
260 $c2020
520 $aThis paper presents the state of the art of the statistical modelling as applied to plant breeding. Classes of inference, statistical models, estimation methods and model selection are emphasized in a practical way. Restricted Maximum Likelihood (REML), Hierarchical Maximum Likelihood (HIML) and Bayesian (BAYES) are highlighted. Distributions of data and effects, and dimension and structure of the models are considered for model selection and parameters estimation. Theory and practical examples referring to selection between models with different fixed effects factors are given using the Full Maximum Likelihood (FML). An analytical FML way of defining random or fixed effects is presented to avoid the subjective or conceptual usual definitions. Examples of the applications of the Hierarchical Maximum Likelihood/Hierarchical Generalized Best Linear Unbiased Prediction (HIML/HG-BLUP) procedure are also presented. Sample sizes for achieving high experimental quality and accuracy are indicated and simple interpretation of the estimates of key genetic parameters are given. Phenomics and genomics are approached. Maximum accuracy under the truest model is the key for achieving efficacy in plant breeding programs.
650 $aPlant breeding
650 $aStatistical models
650 $aMelhoramento Genético Vegetal
650 $aMétodo Estatístico
700 1 $aALVES, R. S.
773 $tFunctional Plant Breeding Journal$gv. 2, n. 2, jul./dez., 2020. p. 1-31.
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