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	Provedor de dados AgEcon (2) Autor Pickles, Andrew (2) Rabe-Hesketh, Sophia (2) Skrondal, Anders (2) Palavra-chave Adaptive quadrature (2) Gllamm (2) Research Methods/ Statistical Methods (2) Adaptive integration (1) Clustered data (1) Cme (1) Congeneric measurement model (1) Covariate measurement error (1) Empirical Bayes (1) Factor model (1) Generalized linear mixed models (1) Latent class model (1) Measurement model (1) Multilevel models (1) NPMLE (1) Nonparametric maximum likelihood (1) Numerical integration (1) Panel data (1) Random-effects models (1) Sensitivity analysis (1) Mais... Tipo do documento Journal Article (2) Ano 2003 (1) 2002 (1) País United States (2) 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 Maximum likelihood estimation of generalized linear models with covariate measurement error AgEcon Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew. Generalized linear models with covariate measurement error can be estimated by maximum likelihood using gllamm, a program that fits a large class of multilevel latent variable models (Rabe-Hesketh, Skrondal, and Pickles 2004). The program uses adaptive quadrature to evaluate the log likelihood, producing more reliable results than many other methods (Rabe-Hesketh, Skrondal, and Pickles 2002). For a single covariate measured with error (assuming a classical measurement model), we describe a “wrapper” command, cme, that calls gllamm to estimate the model. The wrapper makes life easy for the user by accepting a simple syntax and data structure and producing extended and easily interpretable output. The commands for preparing the data and running gllamm can... Tipo: Journal Article Palavras-chave: Covariate measurement error; Measurement model; Congeneric measurement model; Factor model; Adaptive quadrature; Nonparametric maximum likelihood; NPMLE; Latent class model; Empirical Bayes; Simulation; Wrapper; Sensitivity analysis; Gllamm; Cme; Research Methods/ Statistical Methods. Ano: 2003 URL: http://purl.umn.edu/116185 Registros recuperados: 2 Primeira ... 1 ... Última

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