Abstract
This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for niultivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m-dependence and exchangeable correlation structures from each subject are discussed. Efficiency of the pioposecl estimators in two simple situations is considered. The approach is closely related to quasi-likelihood.
Keywords
MathematicsEstimatorEstimating equationsGeneralized estimating equationExtension (predicate logic)Applied mathematicsGeneralized linear modelGaussianIndependence (probability theory)Class (philosophy)Asymptotic distributionSimple (philosophy)Linear modelVariance (accounting)StatisticsLongitudinal dataLinear regressionQuasi-likelihoodCount dataComputer science
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Publication Info
- Year
- 1986
- Type
- article
- Volume
- 73
- Issue
- 1
- Pages
- 13-22
- Citations
- 17707
- Access
- Closed
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Cite This
Kung‐Yee Liang,
Scott L. Zeger
(1986).
Longitudinal data analysis using generalized linear models.
Biometrika
, 73
(1)
, 13-22.
https://doi.org/10.1093/biomet/73.1.13
Identifiers
- DOI
- 10.1093/biomet/73.1.13