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Literatura académica sobre el tema "Models of generalized estimating equations"

Autor: Grafiati
Publicado: 25 de mayo de 2024

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Artículos de revistas sobre el tema "Models of generalized estimating equations"

1

Vens, M., and A. Ziegler. "Generalized Estimating Equations." Methods of Information in Medicine 49, no. 05 (2010): 421–25. http://dx.doi.org/10.3414/me10-01-0026.

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Summary Background: Generalized estimating equations (GEE) are an extension of generalized linear models (GLM) in that they allow adjusting for correlations between observations. A major strength of GEE is that they do not require the correct specification of the multivariate distribution but only of the mean structure. Objectives: Several concerns have been raised about the validity of GEE when applied to dichotomous dependent variables. In this contribution, we summarize the theoretical findings concerning efficiency and validity of GEE. Methods: We introduce the GEE in a formal way, summari
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Feddag, Mohand-Larbi, Ion Grama, and Mounir Mesbah. "Generalized Estimating Equations (GEE) for Mixed Logistic Models." Communications in Statistics - Theory and Methods 32, no. 4 (2003): 851–74. http://dx.doi.org/10.1081/sta-120018833.

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Lo, Chi Ho, Wing Kam Fung, and Zhong Yi Zhu. "Structural Parameter Estimation Using Generalized Estimating Equations for Regression Credibility Models." ASTIN Bulletin 37, no. 02 (2007): 323–43. http://dx.doi.org/10.2143/ast.37.2.2024070.

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A generalized estimating equations (GEE) approach is developed to estimate structural parameters of a regression credibility model with independent or moving average errors. A comprehensive account is given to illustrate how GEE estimators are worked out within an extended Hachemeister (1975) framework. Evidenced by results of simulation studies, the proposed GEE estimators appear to outperform those given by Hachemeister, and have led to a remarkable improvement in accuracy of the credibility estimators so constructed.
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Lo, Chi Ho, Wing Kam Fung, and Zhong Yi Zhu. "Structural Parameter Estimation Using Generalized Estimating Equations for Regression Credibility Models." ASTIN Bulletin 37, no. 2 (2007): 323–43. http://dx.doi.org/10.1017/s0515036100014896.

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A generalized estimating equations (GEE) approach is developed to estimate structural parameters of a regression credibility model with independent or moving average errors. A comprehensive account is given to illustrate how GEE estimators are worked out within an extended Hachemeister (1975) framework. Evidenced by results of simulation studies, the proposed GEE estimators appear to outperform those given by Hachemeister, and have led to a remarkable improvement in accuracy of the credibility estimators so constructed.
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5

Breitung, J., N. R. Chaganty, R. M. Daniel, et al. "Discussion of “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”." Methods of Information in Medicine 49, no. 05 (2010): 426–32. http://dx.doi.org/10.1055/s-0038-1625133.

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Summary Objective: To discuss generalized estimating equations as an extension of generalized linear models by commenting on the paper of Ziegler and Vens “Generalized Estimating Equations: Notes on the Choice of the Working Correlation Matrix”. Methods: Inviting an international group of experts to comment on this paper. Results: Several perspectives have been taken by the discussants. Econometricians have established parallels to the generalized method of moments (GMM). Statisticians discussed model assumptions and the aspect of missing data. Applied statisticians commented on practical aspe
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6

Zubair, Seema, and Sanjoy K. Sinha. "Marginal models for longitudinal count data with dropouts." Journal of Statistical Research 54, no. 1 (2020): 27–42. http://dx.doi.org/10.47302/jsr.2020540102.

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In this article, we investigate marginal models for analyzing incomplete longitudinal count data with dropouts. Specifically, we explore commonly used generalized estimating equations and weighted generalized estimating equations for fitting log-linear models to count data in the presence of monotone missing responses. A series of simulations were carried out to examine the finite-sample properties of the estimators in the presence of both correctly specified and misspecified dropout mechanisms. An application is provided using actual longitudinal survey data from the Health and Retirement Stu
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7

Ma, Yanyuan, and Marc G. Genton. "Explicit estimating equations for semiparametric generalized linear latent variable models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72, no. 4 (2010): 475–95. http://dx.doi.org/10.1111/j.1467-9868.2010.00741.x.

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Corrente, JosÉ Eduardo, and Maria Del Pilar DÍAz. "Ordinal models and generalized estimating equations to evaluate disease severity." Journal of Applied Statistics 30, no. 4 (2003): 425–39. http://dx.doi.org/10.1080/0266476032000035458.

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Koper, Nicola, and Micheline Manseau. "Generalized estimating equations and generalized linear mixed-effects models for modelling resource selection." Journal of Applied Ecology 46, no. 3 (2009): 590–99. http://dx.doi.org/10.1111/j.1365-2664.2009.01642.x.

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Nikita, Efthymia. "The use of generalized linear models and generalized estimating equations in bioarchaeological studies." American Journal of Physical Anthropology 153, no. 3 (2013): 473–83. http://dx.doi.org/10.1002/ajpa.22448.

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