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Provedor de dados:  AgEcon
País:  United States
Título:  Reliable estimation of generalized linear mixed models using adaptive quadrature
Autores:  Rabe-Hesketh, Sophia
Skrondal, Anders
Pickles, Andrew
Data:  2011-09-29
Ano:  2002
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
Resumo:  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 multilevel version of this method in gllamm, a program that fits a large class of multilevel latent variable models including multilevel generalized linear mixed models. As far as we know, this is the first time that adaptive quadrature has been proposed for multilevel models. We show that adaptive quadrature works well in problems where ordinary quadrature fails. Furthermore, even when ordinary quadrature works, adaptive quadrature is often computationally more efficient since it requires fewer quadrature points to achieve the same precision.
Tipo:  Journal Article
Idioma:  Inglês
Identificador:  st0005

http://purl.umn.edu/115947
Relação:  Stata Journal>Volume 2, Number 1, 1st Quarter 2002
Formato:  21
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