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Robust inference for generalized linear models with application to logistic regression

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  • Adimari, Gianfranco
  • Ventura, Laura

Abstract

In this paper we consider a suitable scale adjustment of the estimating function which defines a class of robust M-estimators for generalized linear models. This leads to a robust version of the quasi-profile loglikelihood which allows us to derive robust likelihood ratio type tests for inference and model selection having the standard asymptotic behaviour. An application to logistic regression is discussed.

Suggested Citation

  • Adimari, Gianfranco & Ventura, Laura, 2001. "Robust inference for generalized linear models with application to logistic regression," Statistics & Probability Letters, Elsevier, vol. 55(4), pages 413-419, December.
    • Handle: RePEc:eee:stapro:v:55:y:2001:i:4:p:413-419
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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. Cantoni E. & Ronchetti E., 2001. "Robust Inference for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1022-1030, September.
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    Cited by:

    1. Daniel B. Hall & Jing Shen, 2010. "Robust Estimation for Zero‐Inflated Poisson Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 237-252, June.
    2. Gianfranco Adimari & Laura Ventura, 2002. "Quasi-likelihood fromM-estimators: A numerical comparison with empirical likelihood," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 175-185, June.
    3. Kenneth Rice & David Spiegelhalter, 2006. "A Simple Diagnostic Plot Connecting Robust Estimation, Outlier Detection, and False Discovery Rates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1131-1147.

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