Robust Wald-type tests in GLM with random design based on minimum density power divergence estimators

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...

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Vydané v:	Statistical methods & applications Ročník 30; číslo 3; s. 973 - 1005
Hlavní autori:	Basu, Ayanendranath, Ghosh, Abhik, Mandal, Abhijit, Martin, Nirian, Pardo, Leandro
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021 Springer Nature B.V
Predmet:	Asymptotic properties Chemistry and Earth Sciences Computer Science Density Economics Finance Generalized linear models Health Sciences Humanities Inference Insurance Law Management Mathematics and Statistics Medicine Original Paper Parameter estimation Physics Regression models Robustness (mathematics) Statistical models Statistical Theory and Methods Statistics Statistics for Business Statistics for Engineering Statistics for Life Sciences Statistics for Social Sciences Wald-type tests Minimum density power divergence estimator Robustness Generalized linear models
ISSN:	1618-2510, 1613-981X
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