Contents
How are generalized estimating equations used in data analysis?
Generalized Estimating Equations Introduction The generalized estimating equations (GEEs) methodology, introduced by Liang and Zeger (1986), enables you to analyze correlated data that otherwise could be modeled as a generalized linear model. GEEs have become an important strategy in the analysis of correlated data.
Which is a special case of the generalized method of moments?
The generalized estimating equation is a special case of the generalized method of moments (GMM). This relationship is immediately obvious from the requirement that the score function satisfy the equation:
Why is the Gee called a semiparametric estimator?
Indeed, the GEE unified several independent formulations of these standard error estimators in a general framework. 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.
What are heteroscedasticity consistent standard error estimators?
In the case of a linear model with a working independence variance structure, these are known as “heteroscedasticity consistent standard error” estimators. Indeed, the GEE unified several independent formulations of these standard error estimators in a general framework.
How are correlation structures used in generalized estimating equations?
It uses quasi-likelhood estimation rather than maximum likelihood estimation (MLE) or ordinary least squares (OLS) to estimate the parameters, but at times these will coincide. Covariance specification. These are typically four or more correlation structures that we assume apriori. Here are four correlation structures:
How to model count data as generalized linear equations?
We have learned so far to model the count data as various generalized linear models with a key assumption of independence among the response. GEE approach is an extension of GLMs. It provides a semi-parametric approach to longitudinal analysis of categorical response; it can be also used for continuous measurements.
How are Gee estimates of model parameters obtained?
In general, there are no closed-form solutions, so the GEE estimates are obtained by using an iterative algorithm, that is iterative quasi-scoring procedure. GEE estimates of model parameters are valid even if the covariance is mis-specified (because they depend on the first moment, e.g., mean).