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| Barroso,Laís Mayara Azevedo; Nascimento,Moysés; Barili,Leiri Daiane; Nascimento,Ana Carolina Campana; Vale,Naine Martins do; Silva,Fabyano Fonseca e; Carneiro,José Eustáquio de Souza. |
| ABSTRACT: The aim of this study was to use quantile regression (QR) to characterize the effect of the adaptability parameter throughout the distribution of the productivity variable on black bean cultivars launched by different national research institutes (research centers) over the last 50 years. For this purpose, 40 cultivars developed by Brazilian genetic improvement programs between 1959 and 2013 were used. Initially, QR models were adjusted considering three quantiles (τ = 0.2, 0.5 and 0.8). Subsequently, with the confidence intervals, quantile models τ = 0.2 and 0.8 (QR0.2 and QR0.8) showed differences regarding the parameter of adaptability and average productivity. Finally, by grouping the cultivars into one of the two groups defined from QR0.2... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Gene-environment (GxE) interaction; Phaseolus vulgaris L; Genetic improvement; Quantiles; Regression models. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782019000300200 |
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| Puiatti,Guilherme Alves; Cecon,Paulo Roberto; Nascimento,Moysés; Nascimento,Ana Carolina Campana; Carneiro,Antônio Policarpo Souza; Silva,Fabyano Fonseca e; Puiatti,Mário; Cruz,Cosme Damião. |
| ABSTRACT: The objective of this study was to adjust nonlinear quantile regression models for the study of dry matter accumulation in garlic plants over time, and to compare them to models fitted by the ordinary least squares method. The total dry matter of nine garlic accessions belonging to the Vegetable Germplasm Bank of Universidade Federal de Viçosa (BGH/UFV) was measured in four stages (60, 90, 120 and 150 days after planting), and those values were used for the nonlinear regression models fitting. For each accession, there was an adjustment of one model of quantile regression (τ=0.5) and one based on the least squares method. The nonlinear regression model fitted was the Logistic. The Akaike Information Criterion was used to evaluate the goodness of... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Quantile regression; Nonlinear regression; Allium sativum L.; Growth rate; Cluster analysis.. |
| Ano: 2020 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782020000100203 |
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| Puiatti,Guilherme Alves; Cecon,Paulo Roberto; Nascimento,Moysés; Nascimento,Ana Carolina Campana; Carneiro,Antônio Policarpo Souza; Silva,Fabyano Fonseca e; Puiatti,Mário; Oliveira,Ana Carolina Ribeiro de. |
| ABSTRACT: Plant growth analyses are important because they generate information on the demand and necessary care for each development stage of a plant. Nonlinear regression models are appropriate for the description of curves of growth, since they include parameters with practical biological interpretation. However, these models present information in terms of the conditional mean, and they are subject to problems in the adjustment caused by possible outliers or asymmetry in the distribution of the data. Quantile regression can solve these problems, and it allows the estimation of different quantiles, generating more complete and robust results. The objective of this research was to adjust a nonlinear quantile regression model for the study of dry matter... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Quantile regression; Nonlinear regression; Dry matter; Allium sativum L. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782018000100203 |
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| Santos,Patricia Mendes dos; Nascimento,Ana Carolina Campana; Nascimento,Moysés; Silva,Fabyano Fonseca e; Azevedo,Camila Ferreira; Mota,Rodrigo Reis; Guimarães,Simone Eliza Facioni; Lopes,Paulo Sávio. |
| Abstract: The objective of this work was to evaluate the use of regularized quantile regression (RQR) to predict the genetic merit of pigs for asymmetric carcass traits, compared with the Bayesian lasso (Blasso) method. The genetic data of the traits carcass yield, bacon thickness, and backfat thickness from a F2 population composed of 345 individuals, generated by crossing animals from the Piau breed with those of a commercial breed, were used. RQR was evaluated considering different quantiles (τ = 0.05 to 0.95). The RQR model used to estimate the genetic merit showed accuracies higher than or equal to those obtained by Blasso, for all studies traits. There was an increase of 6.7 and 20.0% in accuracy when the quantiles 0.15 and 0.45 were considered in... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Sus scrofa; Blasso; Shrinkage. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2018000901011 |
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| Barroso,Laís Mayara Azevedo; Nascimento,Moysés; Nascimento,Ana Carolina Campana; Silva,Fabyano Fonseca e; Cruz,Cosme Damião; Bhering,Leonardo Lopes; Ferreira,Reinaldo de Paula. |
| O objetivo deste trabalho foi desenvolver e validar uma metodologia de análise da adaptabilidade e da estabilidade fenotípica baseada em regressão quantílica (RQ). Para tanto, foram simulados valores fenotípicos com distribuição simétrica e com distribuição assimétrica à direita e à esquerda, com ou sem a presença de "outliers". A metodologia proposta foi aplicada a um conjunto de dados provenientes de um experimento com 92 genótipos de alfafa (Medicago sativa), avaliados em 20 ambientes, e comparada às metodologias de Eberhart & Russell e de regressão não paramétrica. A metodologia da RQ proporcionou resultados iguais ou superiores aos obtidos com as metodologias alternativas avaliadas. No entanto, a ocorrência de resultados discordantes entre as... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Medicago sativa; Distribuição assimétrica; Interação genótipo x ambiente; Melhoramento vegetal; Outliers; Regressão não paramétrica.. |
| Ano: 2015 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2015000400290 |
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| Nascimento,Moysés; Silva,Fabyano Fonseca e; Sáfadi,Thelma; Nascimento,Ana Carolina Campana; Ferreira,Reinaldo de Paula; Cruz,Cosme Damião. |
| O objetivo deste trabalho foi propor uma abordagem bayesiana do método de Eberhart & Russell para avaliar a adaptabilidade e da estabilidade fenotípica de genótipos de alfafa (Medicago sativa), bem como avaliar a eficiência da utilização de distribuições a priori informativas e pouco informativas. Foram utilizados dados de um experimento em blocos ao acaso, no qual se avaliou a produção de massa de matéria seca de 92 genótipos. A metodologia bayesiana proposta foi implementada no programa livre R por meio da função MCMCregress do pacote MCMCpack. Para representar as distribuições a priori pouco informativas, utilizaram-se distribuições de probabilidade com grande variância; e, para representar distribuições a priori informativas, adotou-se o... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Medicago sativa; Fator de Bayes; Priori informativa; Interação genótipo x ambiente; MCMC. |
| Ano: 2011 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2011000100004 |
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| Sousa,Ithalo Coelho de; Nascimento,Moysés; Silva,Gabi Nunes; Nascimento,Ana Carolina Campana; Cruz,Cosme Damião; Silva,Fabyano Fonseca e; Almeida,Dênia Pires de; Pestana,Kátia Nogueira; Azevedo,Camila Ferreira; Zambolim,Laércio; Caixeta,Eveline Teixeira. |
| ABSTRACT Genomic selection (GS) emphasizes the simultaneous prediction of the genetic effects of thousands of scattered markers over the genome. Several statistical methodologies have been used in GS for the prediction of genetic merit. In general, such methodologies require certain assumptions about the data, such as the normality of the distribution of phenotypic values. To circumvent the non-normality of phenotypic values, the literature suggests the use of Bayesian Generalized Linear Regression (GBLASSO). Another alternative is the models based on machine learning, represented by methodologies such as Artificial Neural Networks (ANN), Decision Trees (DT) and related possible refinements such as Bagging, Random Forest and Boosting. This study aimed to... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Hemileia vastatrix; Statistical learning; Plant breeding; Artificial intelligence. |
| Ano: 2021 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162021000401102 |
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