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Empirical likelihood for generalized linear models with longitudinal data

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  • Li, Daoji
  • Pan, Jianxin

Abstract

In this paper, empirical likelihood-based inference for longitudinal data within the framework of generalized linear model is investigated. The proposed procedure takes into account the within-subject correlation without involving direct estimation of nuisance parameters in the correlation matrix and retains optimal even if the working correlation structure is misspecified. The proposed approach yields more efficient estimators than conventional generalized estimating equations and achieves the same asymptotic variance as quadratic inference function based methods. Furthermore, hypothesis testing procedures are developed to test whether or not the model assumption is met and whether or not regression coefficients are significant. The finite sample performance of the proposed methods is evaluated through simulation studies. Application to the Ohio Children Wheeze Status data is also discussed.

Suggested Citation

  • Li, Daoji & Pan, Jianxin, 2013. "Empirical likelihood for generalized linear models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 63-73.
    • Handle: RePEc:eee:jmvana:v:114:y:2013:i:c:p:63-73
    DOI: 10.1016/j.jmva.2012.07.014
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    6. Ouyang, Jiangrong & Bondell, Howard, 2023. "Bayesian analysis of longitudinal data via empirical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    7. Yujing Shao & Lei Wang, 2022. "Generalized partial linear models with nonignorable dropouts," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 223-252, February.

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