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Relevant bibliographies by topics / Generalized estimating equations model

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Journal articles

Academic literature on the topic 'Generalized estimating equations model'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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Journal articles on the topic "Generalized estimating equations model"

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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2

Feddag, M.-L. "Generalized Estimating Equations to Binary Probit Model." Communications in Statistics - Theory and Methods 43, no. 19 (2014): 3997–4010. http://dx.doi.org/10.1080/03610926.2012.712186.

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3

Feddag, Mohand L., and Mounir Mesbah. "Generalized estimating equations for longitudinal mixed Rasch model." Journal of Statistical Planning and Inference 129, no. 1-2 (2005): 159–79. http://dx.doi.org/10.1016/j.jspi.2004.06.045.

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4

Carey, Vincent J., and You-Gan Wang. "Working covariance model selection for generalized estimating equations." Statistics in Medicine 30, no. 26 (2011): 3117–24. http://dx.doi.org/10.1002/sim.4300.

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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

Fang, Fang, Jialiang Li, and Jingli Wang. "Optimal model averaging estimation for correlation structure in generalized estimating equations." Communications in Statistics - Simulation and Computation 48, no. 5 (2018): 1574–93. http://dx.doi.org/10.1080/03610918.2017.1419260.

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7

Shen, Chung-Wei, and Yi-Hau Chen. "Model Selection for Generalized Estimating Equations Accommodating Dropout Missingness." Biometrics 68, no. 4 (2012): 1046–54. http://dx.doi.org/10.1111/j.1541-0420.2012.01758.x.

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8

Lipi, Nasrin, Mohammad Samsul Alam, and Syed Shahadat Hossain. "A Generalized Estimating Equations Approach for Modeling Spatially Clustered Data." Austrian Journal of Statistics 50, no. 4 (2021): 36–52. http://dx.doi.org/10.17713/ajs.v50i4.1097.

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Clustering in spatial data is very common phenomena in various fields such as disease mapping, ecology, environmental science and so on. Analysis of spatially clustered data should be different from conventional analysis of spatial data because of the nature of clusters in the data. Because it is expected that the observations of same cluster are more similar than the observations from different clusters. In this study, a method has been proposed for the analysis of spatially clustered areal data based on generalized estimating equations which were originally developed for analyzing longitudin

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9

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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10

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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Abstract:

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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