Robust Generalized Estimating Equations Method and its Application in Correlated Binary Outcomes Models

Document Type : Scientific Research

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Abstract

The generalized estimating equations method of Liang and Zeger (1986) facilitates analysis of data collected in longitudinal, nested, or repeated measures designs. GEEs use the generalized linear model to estimate more efficient and unbiased regression parameters relative to ordinary least squares regression when there is unkown correlation among the observations. This method can be highly influenced by the presence of outliers and looze its efficiency. To reduce the effects of these data a robustified generalized estimating equations for Schweppe and Mallos classes are introduced. Then we will compare these procedures with the unrobused GEE by simulation studies for correlated binary outcomes models. 

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References

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Volume 1, Issue 1 - Serial Number 1
October 2015
Pages 49-59

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