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	Provedor de dados Rev. Ciênc. Agron. (1) AgEcon (1) Autor Coimbra,Jefferson Luís Meirelles (1) Guidolin,Altamir Frederico (1) Melo,Rita Carolina de (1) Pickles, Andrew (1) Rabe-Hesketh, Sophia (1) Santos,Marcio dos (1) Skrondal, Anders (1) Trevisani,Nicole (1) Mais... Palavra-chave Generalized linear mixed models (2) Adaptive integration (1) Adaptive quadrature (1) Analysis of variance (1) Clustered data (1) Crop breeding (1) Gllamm (1) Homogeneity of variance (1) Multilevel models (1) Normality of errors (1) Numerical integration (1) Panel data (1) Random-effects models (1) Research Methods/ Statistical Methods (1) Mais... Tipo do documento Info:eu-repo/semantics/article (1) Journal Article (1) Ano 2020 (1) 2002 (1) País Brazil (1) United States (1) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Reliable estimation of generalized linear mixed models using adaptive quadrature AgEcon Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew. Generalized linear mixed models or multilevel regression models have become increasingly popular. Several methods have been proposed for estimating such models. However, to date there is no single method that can be assumed to work well in all circumstances in terms of both parameter recovery and computational efficiency. Stata’s xt commands for two-level generalized linear mixed models (e.g., xtlogit) employ Gauss–Hermite quadrature to evaluate and maximize the marginal log likelihood. The method generally works very well, and often better than common contenders such as MQL and PQL, but there are cases where quadrature performs poorly. Adaptive quadrature has been suggested to overcome these problems in the two-level case. We have recently implemented a... Tipo: Journal Article Palavras-chave: Adaptive quadrature; Gllamm; Generalized linear mixed models; Random-effects models; Panel data; Numerical integration; Adaptive integration; Multilevel models; Clustered data; Research Methods/ Statistical Methods. Ano: 2002 URL: http://purl.umn.edu/115947 Statistical model assumptions achieved by linear models: classics and generalized mixed Rev. Ciênc. Agron. Melo,Rita Carolina de; Trevisani,Nicole; Santos,Marcio dos; Guidolin,Altamir Frederico; Coimbra,Jefferson Luís Meirelles. ABSTRACT When an agricultural experiment is completed and the data about the response variable is available, it is necessary to perform an analysis of variance. However, the hypothesis testing of this analysis shows validity only if the assumptions of the statistical model are ensured. When such assumptions are violated, procedures must be applied to remedy the problem. The present study aimed to compare and investigate how the assumptions of the statistical model can be achieved by classical linear model and generalized linear mixed model, as well as their impact on the hypothesis test of the analysis of variance. The data used in this study was obtained from a genetic breeding program on the cooking time of segregating populations. The following... Tipo: Info:eu-repo/semantics/article Palavras-chave: Analysis of variance; Homogeneity of variance; Normality of errors; Crop breeding; Generalized linear mixed models. Ano: 2020 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000100415 Registros recuperados: 2 Primeira ... 1 ... Última

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