Prof. Nan Lin
 

Department of Mathematics, Washington University
 

Title: A Finite Mixture Model for Working Correlation Matrices in Generalized Estimating Equations 

Abstract: It is well known that the efficiency of the generalized estimating equations (GEE) estimator can be seriously affected by if the working correlation matrix is misspecified. To address the misspecification issue, we propose a finite mixture model for the working correlation called mix-GEE. Under mild regularity conditions, the mix-GEE estimator is consistent and asymptotically normal. And it is also asymptotically efficient if data are from a Gaussian mixture model. An important feature of the mix-GEE method is that it guarantees the positive definiteness of the estimated working correlation matrix if either the AR(1) or exchangeable structure is included. So, it is numerically more stable and displays better finite sample efficiency than the hybrid GEE method (Leung, Wang and Zhu, 2009). The value of our method is further demonstrated by simulation studies and real data examples.