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Diagnostic measures for empirical likelihood of general estimating equations

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

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    File URL: http://hdl.handle.net/10.1093/biomet/asm094
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    Article provided by Biometrika Trust in its journal Biometrika.

    Volume (Year): 95 (2008)
    Issue (Month): 2 ()
    Pages: 489-507

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    Handle: RePEc:oup:biomet:v:95:y:2008:i:2:p:489-507
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