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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Statistical methods & applications Ročník 30; číslo 3; s. 973 - 1005
Hlavní autoři: Basu, Ayanendranath, Ghosh, Abhik, Mandal, Abhijit, Martin, Nirian, Pardo, Leandro
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021
Springer Nature B.V
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-020-00544-4