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Februrary 2003 Asymptotics for generalized estimating equations with large cluster sizes
Minge Xie, Yaning Yang
Ann. Statist. 31(1): 310-347 (Februrary 2003). DOI: 10.1214/aos/1046294467


Generalized estimating equations are used in regression analysis of longitudinal data, where observations on each subject are correlated. Statistical analysis using such methods is based on the asymptotic properties of regression parameter estimators. This paper presents asymptotic results when either the number of independent subjects or the cluster sizes (the number of observations on each subject) or both go to infinity. A set of (information matrix based) general conditions is developed, which leads to the weak and strong consistency as well as the asymptotic normality of the estimators. Most of the results are parallel to the elegant work of Fahrmeir and Kaufmann on maximum likelihood estimators related to the generalized linear models. The conditions for weak consistency and asymptotic normality are verified for several examples of general interest.


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Minge Xie. Yaning Yang. "Asymptotics for generalized estimating equations with large cluster sizes." Ann. Statist. 31 (1) 310 - 347, Februrary 2003.


Published: Februrary 2003
First available in Project Euclid: 26 February 2003

zbMATH: 1018.62019
MathSciNet: MR1962509
Digital Object Identifier: 10.1214/aos/1046294467

Primary: 62F12 , 62J12

Keywords: cluster correlated observations , Generalized estimation equations (GEE) , infinite cluster sizes , longitudinal data

Rights: Copyright © 2003 Institute of Mathematical Statistics


Vol.31 • No. 1 • Februrary 2003
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