They are commonly used in large epidemiological studies, especially multi-site cohort studies, because they can handle many types of unmeasured dependence between outcomes. They are a popular alternative to the likelihood–based generalized linear mixed model which is more sensitive to variance structure specification. GEEs belong to a class of regression techniques that are referred to as semiparametric because they rely on specification of only the first two moments. Indeed, the GEE unified several independent formulations of these standard error estimators in a general framework. In the case of a linear model with a working independence variance structure, these are known as "heteroscedasticity consistent standard error" estimators. I am currently attempting to calculate the standard errors of a larger-than-memory regression problem, and having to take the matrix inverse to calculate the covariance matrix is making the computation very memory intensive.
GEEs are usually used in conjunction with Huber–White standard error estimates, also known as "robust standard error" or "sandwich variance" estimates. The equations arent very different but we can gain some intuition into the effects of using weighted least squares by looking at a. Compare this with the fitted equation for the ordinary least squares model: Progeny 0.12703 + 0.2100 Parent. The focus of the GEE is on estimating the average response over the population ("population-averaged" effects) rather than the regression parameters that would enable prediction of the effect of changing one or more covariates on a given individual. The resulting fitted equation from Minitab for this model is: Progeny 0.12796 + 0.2048 Parent. Remember they are valid only if homoskedasticity holds. Parameter estimates from the GEE are consistent even when the covariance structure is misspecified, under mild regularity conditions. Under these three assumptions the conditional variance-covariance matrix of OLS estimator is E(( )( )X) 2(XX)1 (8) By default command reg uses formula (8) to report standard error, t value, etc. In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unknown correlation between outcomes.