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Local Influence in Generalized Estimating Equations

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  • KANG‐MO JUNG

Abstract

. We investigate the influence of subjects or observations on regression coefficients of generalized estimating equations (GEEs) using local influence. The GEE approach does not require the full multivariate distribution of the response vector. We extend the likelihood displacement to a quasi‐likelihood displacement, and propose local influence diagnostics under several perturbation schemes. An illustrative example in GEEs is given and we compare the results using the local influence and deletion methods.

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  • Kang‐Mo Jung, 2008. "Local Influence in Generalized Estimating Equations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 286-294, June.
    • Handle: RePEc:bla:scjsta:v:35:y:2008:i:2:p:286-294
    DOI: 10.1111/j.1467-9469.2007.00575.x
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    Cited by:

    1. D. T. Nava & F. De Bastiani & M. A. Uribe-Opazo & O. Nicolis & M. Galea, 2017. "Local Influence for Spatially Correlated Binomial Data: An Application to the Spodoptera frugiperda Infestation in Corn," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 540-561, December.
    2. Vens, Maren & Ziegler, Andreas, 2012. "Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1232-1242.
    3. Hattori, Satoshi & Kato, Mai, 2009. "Approximate subject-deletion influence diagnostics for Inverse Probability of Censoring Weighted (IPCW) method," Statistics & Probability Letters, Elsevier, vol. 79(17), pages 1833-1838, September.
    4. Manghi, Roberto F. & Cysneiros, Francisco José A. & Paula, Gilberto A., 2019. "Generalized additive partial linear models for analyzing correlated data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 47-60.

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