Modeling Covariance Structure for Longitudinal Data

• Activity Number: 450 - Inference with Clustered Data: Lessons from Multiple Disciplines
• Type: Invited
• Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Survey Research Methods Section
• Author(s): Annie Qu*
• Companies: University of Illinois at Urbana-Champaign
• Keywords: Missing not at random; Non-monotone missing pattern; Quadratic inference function; Survey data; Working correlation; Generalized estimating equation

Abstract

In this talk, I will provide an overview of using the quadratic inference function for analyzing longitudinal or clustered data, where a working covariance matrix is used to weight the contribution of moment conditions from cluster data. Provided that the fixed effect part of the model is correctly specified, the estimates are consistent regardless of the choice of the weighting matrix. However, we can gain estimation efficiency if the working covariance matrix approximates the actual covariance of the observation-wise residuals sufficiently well. The working covariance matrix can be represented by a linear combination of several known basis matrices with a small number of parameters to increase the asymptotic efficiency of the fixed-effect estimation. In addition, I will show how the method works for non-ignorable missing data which arise frequently in survey research.