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Assessing robustness of generalised estimating equations and quadratic inference functions

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  • Annie Qu

Abstract

In the presence of data contamination or outliers, some empirical studies have indicated that the two methods of generalised estimating equations and quadratic inference functions appear to have rather different robustness behaviour. This paper presents a theoretical investigation from the perspective of the influence function to identify the causes for the difference. We show that quadratic inference functions lead to bounded influence functions and the corresponding M-estimator has a redescending property, but the generalised estimating equation approach does not. We also illustrate that, unlike generalised estimating equations, quadratic inference functions can still provide consistent estimators even if part of the data is contaminated. We conclude that the quadratic inference function is a preferable method to the generalised estimating equation as far as robustness is concerned. This conclusion is supported by simulations and real-data examples. Copyright Biometrika Trust 2004, Oxford University Press.

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  • Annie Qu, 2004. "Assessing robustness of generalised estimating equations and quadratic inference functions," Biometrika, Biometrika Trust, vol. 91(2), pages 447-459, June.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:2:p:447-459
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    Cited by:

    1. Tian, Ruiqin & Xue, Liugen & Xu, Dengke, 2016. "Automatic variable selection for varying coefficient models with longitudinal data," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 84-90.
    2. Zhao, Weihua & Lian, Heng & Song, Xinyuan, 2017. "Composite quantile regression for correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 15-33.
    3. Han, Peisong & Song, Peter X.-K., 2011. "A note on improving quadratic inference functions using a linear shrinkage approach," Statistics & Probability Letters, Elsevier, vol. 81(3), pages 438-445, March.
    4. L. Xue & L. Wang & A. Qu, 2010. "Incorporating Correlation for Multivariate Failure Time Data When Cluster Size Is Large," Biometrics, The International Biometric Society, vol. 66(2), pages 393-404, June.
    5. Guo You Qin & Zhong Yi Zhu, 2009. "Robustified Maximum Likelihood Estimation in Generalized Partial Linear Mixed Model for Longitudinal Data," Biometrics, The International Biometric Society, vol. 65(1), pages 52-59, March.
    6. L. L. Henn, 2022. "Limitations and performance of three approaches to Bayesian inference for Gaussian copula regression models of discrete data," Computational Statistics, Springer, vol. 37(2), pages 909-946, April.
    7. Loni Philip Tabb & Eric J. Tchetgen Tchetgen & Greg A. Wellenius & Brent A. Coull, 2016. "Marginalized Zero-Altered Models for Longitudinal Count Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 181-203, October.
    8. Tang, Nian-Sheng & Duan, Xing-De, 2014. "Bayesian influence analysis of generalized partial linear mixed models for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 86-99.
    9. Osorio, Felipe & Gárate, Ángelo & Russo, Cibele M., 2024. "The gradient test statistic for outlier detection in generalized estimating equations," Statistics & Probability Letters, Elsevier, vol. 209(C).

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