Home Itens selecionados Provedores de dados OAI Créditos Sobre Ajuda

			Busca avançada
	Provedor de dados AgEcon (7) Autor Rabe-Hesketh, Sophia (4) Pickles, Andrew (2) Skrondal, Anders (2) Cox, Nicholas J. (1) Miranda, Alfonso (1) Winter, Nicholas J.G. (1) Wolfe, Rory (1) Zheng, Xiaohui (1) Mais... Palavra-chave Gllamm (7) Research Methods/ Statistical Methods (7) Adaptive quadrature (4) Wrapper (2) Adaptive integration (1) Binary variable (1) Clustered data (1) Cme (1) Congeneric measurement model (1) Count data (1) Covariate measurement error (1) Empirical Bayes (1) Endogenous switching (1) Factor model (1) Generalized latent variable model (1) Generalized linear latent and mixed model (1) Generalized linear mixed models (1) Gllapred (1) Introductory (1) Item response theory (1) Mais... Tipo do documento Journal Article (6) Article (1) Ano 2007 (1) 2006 (2) 2004 (2) 2003 (1) 2002 (1) País United States (7) Idioma Inglês (7)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 7 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 Review of Statistical Evaluation of Measurement Errors by Dunn AgEcon Cox, Nicholas J.. This article reviews Statistical Evaluation of Measurement Errors by Dunn. Tipo: Journal Article Palavras-chave: Measurement errors; Linear models; Mixed models; Gllamm; Research Methods/ Statistical Methods. Ano: 2004 URL: http://purl.umn.edu/116280 Estimating parameters of dichotomous and ordinal item response models with gllamm AgEcon Zheng, Xiaohui; Rabe-Hesketh, Sophia. Item response theory models are measurement models for categorical responses. Traditionally, the models are used in educational testing, where responses to test items can be viewed as indirect measures of latent ability. The test items are scored either dichotomously (correct–incorrect) or by using an ordinal scale (a grade from poor to excellent). Item response models also apply equally for measurement of other latent traits. Here we describe the one- and two-parameter logit models for dichotomous items, the partial-credit and rating scale models for ordinal items, and an extension of these models where the latent variable is regressed on explanatory variables. We show how these models can be expressed as generalized linear latent and mixed models and... Tipo: Article Palavras-chave: Gllamm; Gllapred; Latent variables; Rasch model; Partial-credit model; Rating scale model; Latent regression; Generalized linear latent and mixed model; Adaptive quadrature; Item response theory; Research Methods/ Statistical Methods. Ano: 2007 URL: http://purl.umn.edu/119279 Review of A Handbook of Statistical Analyses Using Stata by Rabe-Hesketh and Everitt AgEcon Winter, Nicholas J.G.. This article reviews A Handbook of Statistical Analyses Using Stata by Rabe-Hesketh and Everitt. Tipo: Journal Article Palavras-chave: Introductory; Gllamm; Stata texts; Research Methods/ Statistical Methods. Ano: 2004 URL: http://purl.umn.edu/116253 Maximum likelihood estimation of endogenous switching and sample selection models for binary, ordinal, and count variables AgEcon Miranda, Alfonso; Rabe-Hesketh, Sophia. Studying behavior in economics, sociology, and statistics often involves fitting models in which the response variable depends on a dummy variable—also known as a regime-switch variable—or in which the response variable is observed only if a particular selection condition is met. In either case, standard regression techniques deliver inconsistent estimators if unobserved factors that affect the response are correlated with unobserved factors that affect the switching or selection variable. Consistent estimators can be obtained by maximum likelihood estimation of a joint model of the outcome and switching or selection variable. This article describes a “wrapper” program, ssm, that calls gllamm (Rabe-Hesketh, Skrondal, and Pickles, GLLAMM Manual [University... Tipo: Journal Article Palavras-chave: Endogenous switching; Sample selection; Binary variable; Count data; Ordinal variable; Probit; Poisson regression; Adaptive quadrature; Gllamm; Wrapper; Ssm; Research Methods/ Statistical Methods. Ano: 2006 URL: http://purl.umn.edu/117582 Review of Multilevel and Longitudinal Modeling Using Stata by Rabe-Hesketh and Skrondal AgEcon Wolfe, Rory. This article reviews Multilevel and Longitudinal Modeling Using Stata, by Rabe-Hesketh and Skrondal. Tipo: Journal Article Palavras-chave: Longitudinal; Multilevel; Gllamm; Generalized latent variable model; Research Methods/ Statistical Methods. Ano: 2006 URL: http://purl.umn.edu/117563 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: 7 Primeira ... 1 ... Última

Empresa Brasileira de Pesquisa Agropecuária - Embrapa Todos os direitos reservados, conforme Lei n° 9.610 Política de Privacidade Área restrita		Embrapa Parque Estação Biológica - PqEB s/n° Brasília, DF - Brasil - CEP 70770-901 Fone: (61) 3448-4433 - Fax: (61) 3448-4890 / 3448-4891 SAC: https://www.embrapa.br/fale-conosco