Asymptotic results with generalized estimating equations for longitudinal data

2005/04/01 by R. M. Balan, I. Schiopu-Kratina · 2 citations
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #math.ST #msc:62F12 #msc:62J12. #stat.TH

paper · pdf · doi:10.1214/009053604000001255

published as Annals of Statistics 2005, Vol. 33, No. 2, 522-541 · Published at http://dx.doi.org/10.1214/009053604000001255 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

openalex publication_date 2005/04/01 · arxiv created 2005/05/27 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

We consider the marginal models of Liang and Zeger [Biometrika 73 (1986) 13–22] for the analysis of longitudinal data and we develop a theory of statistical inference for such models. We prove the existence, weak consistency and asymptotic normality of a sequence of estimators defined as roots of pseudo-likelihood equations.

Citations

• Consistency and Asymptotic Normality of the Maximum Likelihood Estimator in Generalized Linear Models
• Longitudinal data analysis using generalized linear models

Cited by

• Fast Computational Methods for Regularized Estimating Equations
• GEE analysis of clustered binary data with diverging number of covariates