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Alternative computational formulae for generalized linear model diagnostics: identifying influential observations with SAS software

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  • Preisser, John S.
  • Garcia, Daniel I.

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  • Preisser, John S. & Garcia, Daniel I., 2005. "Alternative computational formulae for generalized linear model diagnostics: identifying influential observations with SAS software," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 755-764, April.
    • Handle: RePEc:eee:csdana:v:48:y:2005:i:4:p:755-764
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    References listed on IDEAS

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    1. John S. Preisser & Bahjat F. Qaqish, 1999. "Robust Regression for Clustered Data with Application to Binary Responses," Biometrics, The International Biometric Society, vol. 55(2), pages 574-579, June.
    2. D. A. Williams, 1987. "Generalized Linear Model Diagnostics Using the Deviance and Single Case Deletions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(2), pages 181-191, June.
    3. Fay M. P., 2002. "Measuring a Binary Responses Range of Influence in Logistic Regression," The American Statistician, American Statistical Association, vol. 56, pages 5-9, February.
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    Cited by:

    1. 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.
    2. Hammill, Bradley G. & Preisser, John S., 2006. "A SAS/IML software program for GEE and regression diagnostics," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1197-1212, November.
    3. M. Revan Özkale & Stanley Lemeshow & Rodney Sturdivant, 2018. "Logistic regression diagnostics in ridge regression," Computational Statistics, Springer, vol. 33(2), pages 563-593, June.

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