Robust Wald-type tests in GLM with random design based on minimum density power divergence estimators
Article Type
Research Article
Publication Title
Statistical Methods and Applications
Abstract
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.
First Page
973
Last Page
1005
DOI
10.1007/s10260-020-00544-4
Publication Date
9-1-2021
Recommended Citation
Basu, Ayanendranath; Ghosh, Abhik; Mandal, Abhijit; Martin, Nirian; and Pardo, Leandro, "Robust Wald-type tests in GLM with random design based on minimum density power divergence estimators" (2021). Journal Articles. 1836.
https://digitalcommons.isical.ac.in/journal-articles/1836