Autori: Basu, Ayanendranath, Pardo, Leandro, Ghosh, Abhik, Mandal, Abhijit , Martin, Nirian
Titolo: Robust Wald-type tests in GLM with random design based on minimum density power divergence estimators
Periodico: Statistical methods & applications : Journal of the Italian Statistical Society
Anno: 2021 - Volume: 30 - Fascicolo: 3 - Pagina iniziale: 973 - Pagina finale: 1005

We consider the problem of robust inference under the generalized linear model (GLM) with stochastic covariates. We derive the properties of the minimum density power divergence estimator of the parameters in GLM with random design and use this estimator to propose robust Wald-type tests for testing any general composite null hypothesis about the GLM. The asymptotic and robustness properties of the proposed tests are also examined for the GLM with random design. Application of the proposed robust inference procedures to the popular Poisson regression model for analyzing count data is discussed in detail both theoretically and numerically through simulation studies and real data examples.




SICI: 1618-2510(2021)30:3<973:RWTIGW>2.0.ZU;2-7

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