|
|
|
|
| |
|
|
| Silva,Delvan Alves da; Silva,Fabyano Fonseca e; Ventura,Henrique Torres; Junqueira,Vinícius Silva; Silva,Alessandra Alves da; Mota,Rodrigo Reis; Lopes,Paulo Sávio. |
| Abstract: The objective of this work was to evaluate the criteria for the formation of contemporary groups (CGs) in the genetic evaluation of body weight at weaning in Nellore cattle. A total of 713,474 records from 3,066 herds located in Midwestern and Northern Brazil were used. Data were obtained from the genealogical registry of zebu breeds of the Brazilian association of zebu breeders. Data structures were defined based on the number of standard deviations (SDs) for outlier removal (±2.0, ±2.5, ±3.0, and ±3.5) and on the minimal number of animals per CG (3, 7, and 15). Genetic evaluation was performed with an animal model using Bayesian inference. Data structures with ±3.5 SDs and CG with at least 15 animals presented the highest additive genetic... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Heritability; Outliers; Zebu.. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2017000800643 |
| |
|
|
| Meynard, Christine N.; Kaplan, David; Leroy, Boris. |
| Liu et al. (2018) used a virtual species approach to test the effects of outliers on species distribution models. In their simulations, they applied a threshold value over the simulated suitabilities to generate the species distributions, suggesting that using a probabilistic simulation approach would have been more complex and yield the same results. Here, we argue that using a probabilistic approach is not necessarily more complex and may significantly change results. Although the threshold approach may be justified under limited circumstances, the probabilistic approach has multiple advantages. First, it is in line with ecological theory, which largely assumes non‐threshold responses. Second, it is more general, as it includes the threshold as a... |
| Tipo: Text |
Palavras-chave: ENM; Observation errors; Outliers; Prevalence; Probabilistic approach; Sample bias; Simulations; Species distribution models; Virtual ecology; Virtual species. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00501/61309/64868.pdf |
| |
|
| |
|
|
| Freese, Jeremy. |
| This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program least likely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, least likely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negative outcome that had the lowest predicted probabilities of a negative outcome. These can be considered the observations in which the outcome is most surprising given... |
| Tipo: Journal Article |
Palavras-chave: Outliers; Predicted probabilities; Categorical dependent variables; Logistic regression; Research Methods/ Statistical Methods. |
| Ano: 2002 |
URL: http://purl.umn.edu/116014 |
| |
|
|
| Grzegozewski,Denise M; Uribe-Opaz,Miguel A; De Bastiani,Fernanda; Galea,Manuel. |
| D.M. Grzegozewski, M.A. Uribe-Opazo, F. De Bastiani, and M. Galea. 2013. Local influence when fitting Gaussian spatial linear models: an agriculture application. Cien. Inv. Agr. 40(3): 523-535. Outliers can adversely affect how data fit into a model. Obviously, an analysis of dependent data is different from that of independent data. In the latter, i.e., in cases involving spatial data, local outliers can differ from the data in the neighborhood. In this article, we used the local influence technique to identify influential points in the response variables using two different schemes of perturbations. We applied this technique to soil chemical properties and soybean yield. We evaluated the effects of the influential points on the spatial model selection,... |
| Tipo: Journal article |
Palavras-chave: Geostatistical; Influence diagnostics; Maximum likelihood; Outliers; Spatial variability. |
| Ano: 2013 |
URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-16202013000300006 |
| |
|
|
| 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 |
| |
|
|
|
