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Diagnostic measures for empirical likelihood of general estimating equations


  • Hongtu Zhu
  • Joseph G. Ibrahim
  • Niansheng Tang
  • Heping Zhang


We develop diagnostic measures for assessing the influence of individual observations when using empirical likelihood with general estimating equations, and we use these measures to construct goodness-of-fit statistics for testing possible misspecification in the estimating equations. Our diagnostics include case-deletion measures, local influence measures and pseudo-residuals. Our goodness-of-fit statistics include the sum of local influence measures and the processes of pseudo-residuals. Simulation studies are conducted to evaluate our methods, and real datasets are analyzed to illustrate the use of our diagnostic measures and goodness-of-fit statistics. Copyright 2008, Oxford University Press.

Suggested Citation

  • Hongtu Zhu & Joseph G. Ibrahim & Niansheng Tang & Heping Zhang, 2008. "Diagnostic measures for empirical likelihood of general estimating equations," Biometrika, Biometrika Trust, vol. 95(2), pages 489-507.
  • Handle: RePEc:oup:biomet:v:95:y:2008:i:2:p:489-507

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    Cited by:

    1. Venezuela, Maria Kelly & Sandoval, Mônica Carneiro & Botter, Denise Aparecida, 2011. "Local influence in estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1867-1883, April.
    2. repec:eee:econom:v:202:y:2018:i:1:p:57-74 is not listed on IDEAS
    3. repec:spr:stmapp:v:26:y:2017:i:4:d:10.1007_s10260-017-0382-2 is not listed on IDEAS
    4. Vasconcellos, Klaus L.P. & Zea Fernandez, L.M., 2009. "Influence analysis with homogeneous linear restrictions," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3787-3794, September.
    5. Zhang, Yan-Qing & Tang, Nian-Sheng, 2017. "Bayesian local influence analysis of general estimating equations with nonignorable missing data," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 184-200.

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