scholarly journals Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences

Ethology ◽  
2017 ◽  
Vol 124 (2) ◽  
pp. 86-93 ◽  
Author(s):  
Stano Pekár ◽  
Marek Brabec
Keyword(s):  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Correlated Data ◽  
Flexible Approach ◽  
Behavioural Sciences ◽  
Glm Modelling ◽  
Generalized Estimating

An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals

2004 ◽  
Vol 29 (4) ◽  
pp. 421-437 ◽  
Author(s):  
Paolo Ghisletta ◽  
Dario Spini
Keyword(s):  
Social Sciences ◽  
Longitudinal Study ◽  
Generalized Estimating Equations ◽  
Oldest Old ◽  
Estimating Equations ◽  
Correlated Data ◽  
Regression Coefficients ◽  
Unbiased Estimation ◽  
The Social ◽  
Generalized Estimating

Correlated data are very common in the social sciences. Most common applications include longitudinal and hierarchically organized (or clustered) data. Generalized estimating equations (GEE) are a convenient and general approach to the analysis of several kinds of correlated data. The main advantage of GEE resides in the unbiased estimation of population-averaged regression coefficients despite possible misspecification of the correlation structure. This article aims to provide a concise, nonstatistical introduction to GEE. To illustrate the method, an analysis of selectivity effects in the Swiss Interdisciplinary Longitudinal Study on the Oldest Old is presented.

scholarly journals A guide to developing resource selection functions from telemetry data using generalized estimating equations and generalized linear mixed models

Rangifer ◽  
2012 ◽  
Vol 32 (2) ◽  
pp. 195 ◽  
Author(s):  
Nicola Koper ◽  
Micheline Manseau
Keyword(s):  
Generalized Estimating Equations ◽  
Resource Selection ◽  
Statistical Significance ◽  
Estimating Equations ◽  
Correlated Data ◽  
Standard Errors ◽  
Parameter Estimates ◽  
Resource Selection Functions ◽  
Generalized Linear Mixed Effects ◽  
Generalized Estimating

Resource selection functions (RSF) are often developed using satellite (ARGOS) or Global Positioning System (GPS) telemetry datasets, which provide a large amount of highly correlated data. We discuss and compare the use of generalized linear mixed-effects models (GLMM) and generalized estimating equations (GEE) for using this type of data to develop RSFs. GLMMs directly model differences among caribou, while GEEs depend on an adjustment of the standard error to compensate for correlation of data points within individuals. Empirical standard errors, rather than model-based standard errors, must be used with either GLMMs or GEEs when developing RSFs. There are several important differences between these approaches; in particular, GLMMs are best for producing parameter estimates that predict how management might influence individuals, while GEEs are best for predicting how management might influence populations. As the interpretation, value, and statistical significance of both types of parameter estimates differ, it is important that users select the appropriate analytical method. We also outline the use of k-fold cross validation to assess fit of these models. Both GLMMs and GEEs hold promise for developing RSFs as long as they are used appropriately.

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