On gee-based regression estimators under first moment misspegification

Many of the classical estimation methods of statistics lead to estirnators that solve an equation. In common special cases, estimating equationbased estimators have appeal because they correspond to the maximum or minimum of an objective function. In such cases, an intuitively reasonable criterion for estimation, such as minimizing the Euclidean distance between the observation vector and the fitted value. motivates the procrdure. In general. however, solutions of estimating equations need not minimize an objective function. Therefore, when the assumed model for the data is inaccurate, it is unclear what aspect of the data is being described by an estimating equation-based estimator. Since the landmark article of Liang and Zeger (1986), there has been considerable interest in using estimating equations for longitudinal and other clustered data. In this paper we examine the form of the regression parameter estinland under model misspecification when estimating equations related to Liang and Zeger's generalized estimating equations (GEES) are used for model fitting. Closed form expressions are presented for these estimands in simple examples. These results indicate that Copyrght