Asymptotic results with generalized estimating equations for longitudinal data
By R. Balan, Ioana Schiopu-Kratina. 2005
55 citations recorded by the source corpus.
We consider the marginal models of Liang and Zeger (1986) 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.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/aa7a4142ac9cedd3a6ee6faf9fa53acfadad5235
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Statistics and Probability
Citations in the corpus
- GEE analysis of clustered binary data with diverging number of covariates (2011, methodology): “Liang and Zeger (1986) assumes that the estimator of the working correlation matrix parameter τ̂ satisfies √ n(τ̂ − τ 0) = Op(1) for some τ0; assumption (C2) of Chen and Jin (2006) is of similar nature; while Xie and Yang (2003) assumes the nuisance parameter τ to be completely known for simplicity.”
- Consistent model selection and data‐driven smooth tests for longitudinal data in the estimating equations approach (2009, methodology): “(2000) introduced the QIF to improve the efficiency of GEEs; Balan and Schiopu-Kratina (2005) derived a two-step estimation procedure for the marginal model based on the pseudolikelihood and Chiou and Müller (2005) developed a new marginal approach based on semiparametric quasi-likelihood regression”