Penalized Generalized Estimating Equations for High-Dimensional Longitudinal Data Analysis

Lan Wang, Jianhui Zhou, Annie Qu

Research output: Contribution to journalArticlepeer-review

130 Scopus citations


We consider the penalized generalized estimating equations (GEEs) for analyzing longitudinal data with high-dimensional covariates, which often arise in microarray experiments and large-scale health studies. Existing high-dimensional regression procedures often assume independent data and rely on the likelihood function. Construction of a feasible joint likelihood function for high-dimensional longitudinal data is challenging, particularly for correlated discrete outcome data. The penalized GEE procedure only requires specifying the first two marginal moments and a working correlation structure. We establish the asymptotic theory in a high-dimensional framework where the number of covariatespnincreases as the number of clustersnincreases, andpncan reach the same order asn. One important feature of the new procedure is that the consistency of model selection holds even if the working correlation structure is misspecified. We evaluate the performance of the proposed method using Monte Carlo simulations and demonstrate its application using a yeast cell-cycle gene expression data set.

Original languageEnglish (US)
Pages (from-to)353-360
Number of pages8
Issue number2
StatePublished - Jun 2012


  • Correlated data
  • Diverging number of parameters
  • GEE
  • High-dimensional covariates
  • Longitudinal data
  • Marginal regression
  • Variable selection


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