Results 281 to 290 of about 318,594 (329)

Generalized Estimating Equations

2002
Correlated datasets develop when multiple observations are collected from a sampling unit (e.g., repeated measures of a bank over time, or hormone levels in a breast cancer patient over time), or from clustered data where observations are grouped based on a shared characteristic (e.g., observations on different banks grouped by zip code, or on cancer ...
James W. Hardin, Joseph M. Hilbe
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Generalized Estimating Equations

Journal of the American Statistical Association, 2004
(2004). Generalized Estimating Equations. Journal of the American Statistical Association: Vol. 99, No. 465, pp. 297-298.
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Generalized Estimating Equation

2017
The generalized estimating equation (GEE) uses a quasi-likelihood approach for analyzing data with correlated outcomes. This is an extension of GLM and uses quasi-likelihood method for cluster or repeated outcomes. If observations on outcome variable are repeated, it is likely that the observations are correlated.
M. Ataharul Islam, Rafiqul I. Chowdhury
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SAGA Application for Generalized Estimating Equations Analysis

2023
Logistic regression models seek to identify the influence of different variables/factors on a response variable of interest. These are normally used in the field of medicine as it allows verifying which factors influence the presence of certain pathologies. However, most of these models do not consider the correlation between the variables under study.
Luís Moncaixa, Ana Cristina Braga
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Performance of Generalized Estimating Equations in Practical Situations

Biometrics, 1994
Moment methods for analyzing repeated binary responses have been proposed by Liang and Zeger (1986, Biometrika 73, 13-22), and extended by Prentice (1988, Biometrics 44, 1033-1048). In their generalized estimating equations (GEE), both Liang and Zeger (1986) and Prentice (1988) estimate the parameters associated with the expected value of an individual'
Lipsitz, Stuart R., Fitzmaurice, Garrett M., Orav, Endel J., Laird, Nan M.   +3 more
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Local Influence in Generalized Estimating Equations

Scandinavian Journal of Statistics, 2007
Abstract. We investigate the influence of subjects or observations on regression coefficients of generalized estimating equations (GEEs) using local influence. The GEE approach does not require the full multivariate distribution of the response vector. We extend the likelihood displacement to a quasi‐likelihood displacement, and propose local influence
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Akaike's Information Criterion in Generalized Estimating Equations

Biometrics, 2001
Summary. Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model‐selection criteria available in GEE. The well‐known Akaike Information Criterion (AIC)
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Generalized Estimating Equations Logistic Regression

2015
Many fields of study use longitudinal datasets, which usually consist of repeated measurements of a response variable, often accompanied by a set of covariates for each of the subjects/units. However, longitudinal datasets are problematic because they inherently show correlation due to a subject’s repeated set of measurements.
Jeffrey R. Wilson, Kent A. Lorenz
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Generalized Estimating Equation and Generalized Linear Mixed Models

2020
This chapter covers methods to model observations that are correlated. The modeling of correlated data requires an alternative to the joint likelihood to obtain the parameter estimates. One such method is based on modeling the marginal mean and another method is based on modeling the conditional mean.
Jeffrey R. Wilson, Elsa Vazquez-Arreola, Ding-Geng Chen   +2 more
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Diagnostic techniques in generalized estimating equations

Journal of Statistical Computation and Simulation, 2007
We consider herein diagnostic methods for the quasi-likelihood regression models developed by Zeger and Liang [Zeger, S. L., Liang, K.-Y., 1986, Longitudinal data analysis for discrete and conti-nuous outcomes. Biometrics, 42, 121–130.] to analyse discrete and continuous longitudinal data. Our proposal generalises well-known measures (projection matrix,
Maria Kelly Venezuela, Denise Aparecida Botter, Mônica Carneiro Sandoval   +2 more
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