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Robust tests in generalized linear models with missing responses

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  • Bianco, Ana M.
  • Boente, Graciela
  • Rodrigues, Isabel M.

Abstract

In many situations, data follow a generalized linear model in which the mean of the responses is modelled, through a link function, linearly on the covariates. Robust estimators for the regression parameter in order to build test statistics for this parameter, when missing data occur in the responses, are considered. The asymptotic behaviour of the robust estimators for the regression parameter is obtained, under the null hypothesis and under contiguous alternatives. This allows us to derive the asymptotic distribution of the robust Wald-type test statistics constructed from the proposed estimators. The influence function of the test statistics is also studied. A simulation study allows us to compare the behaviour of the classical and robust tests, under different contamination schemes. Applications to real data sets enable to investigate the sensitivity of the p-value to the missing scheme and to the presence of outliers.

Suggested Citation

  • Bianco, Ana M. & Boente, Graciela & Rodrigues, Isabel M., 2013. "Robust tests in generalized linear models with missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 80-97.
    • Handle: RePEc:eee:csdana:v:65:y:2013:i:c:p:80-97
    DOI: 10.1016/j.csda.2012.05.008
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    References listed on IDEAS

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    Cited by:

    1. Ayanendranath Basu & Abhik Ghosh & Abhijit Mandal & Nirian Martin & Leandro Pardo, 2021. "Robust Wald-type tests in GLM with random design based on minimum density power divergence estimators," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 973-1005, September.

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