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Improved wrong-model inference for generalized linear models for binary responses in the presence of link misspecification

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  • Xianzheng Huang

    (University of South Carolina)

Abstract

In the framework of generalized linear models for binary responses, we develop parametric methods that yield estimators for regression coefficients less compromised by an inadequate posited link function. The improved inference are obtained without correcting a misspecified model, and thus are referred to as wrong-model inference. A byproduct of the proposed methods is a simple test for link misspecification in this class of models. Impressive bias reduction in estimators for the regression coefficients from the proposed methods and promising power of the proposed test to detect link misspecification are demonstrated in simulation studies. We also apply these methods to a classic data example frequently analyzed in the existing literature concerning this class of models.

Suggested Citation

  • Xianzheng Huang, 2021. "Improved wrong-model inference for generalized linear models for binary responses in the presence of link misspecification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 437-459, June.
  • Handle: RePEc:spr:stmapp:v:30:y:2021:i:2:d:10.1007_s10260-020-00529-3
    DOI: 10.1007/s10260-020-00529-3
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    References listed on IDEAS

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    1. Fumiko Samejima, 2000. "Logistic positive exponent family of models: Virtue of asymmetric item characteristic curves," Psychometrika, Springer;The Psychometric Society, vol. 65(3), pages 319-335, September.
    2. Daryl Pregibon, 1980. "Goodness of Link Tests for Generalized Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 15-24, March.
    3. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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