Individual-specific, sparse inverse covariance estimation in generalized estimating equations

Abstract

This paper proposes a data-driven approach that derives individual-specific sparse working correlation matrices for generalized estimating equations (GEEs). The approach is motivated by the observation that, in some applications of the GEE, the covariance structure across individuals is heterogeneous and cannot be appropriately captured by a single correlation matrix. The proposed approach enjoys both favorable computational and asymptotic properties. Simulation experiments and analysis of intensively measured longitudinal data on 158 participants collected from a dietary and emotion study are presented.