A Wald-type test statistic for testing linear hypothesis in logistic regression models based on minimum density power divergence estimator

In this paper a robust version of the classical Wald test statistics for linear hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered...

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Detalhes bibliográficos
Autores: Basu, Ayanendranath, Ghosh, Abhik, Mandal, Abhijit, Martín Apaolaza, Nirian, Pardo Llorente, Leandro
Formato: artículo
Fecha de publicación:2017
País:España
Recursos:Universidad Complutense de Madrid (UCM)
Repositorio:Docta Complutense
Idioma:inglés
OAI Identifier:oai:docta.ucm.es:20.500.14352/105569
Acesso em linha:https://hdl.handle.net/20.500.14352/105569
Access Level:acceso abierto
Palavra-chave:Influence function
Logistic regression
Minimum density power divergence estimators
Random explanatory variables
Robustness
Wald-type test statistics
Estadística matemática (Matemáticas)
1209 Estadística
Descrição
Resumo:In this paper a robust version of the classical Wald test statistics for linear hypothesis in the logistic regression model is introduced and its properties are explored. We study the problem under the assumption of random covariates although some ideas with non random covariates are also considered. A family of robust Wald type tests are considered here, where the minimum density power divergence estimator is used instead of the maximum likelihood estimator. We obtain the asymptotic distribution and also study the robustness properties of these Wald type test statistics. The robustness of the tests is investigated theoretically through the influence function analysis as well as suitable practical examples. It is theoretically established that the level as well as the power of the Wald-type tests are stable against contamination, while the classical Wald type test breaks down in this scenario. Some classical examples are presented which numerically substantiate the theory developed. Finally a simulation study is included to provide further confirmation of the validity of the theoretical results established in the paper.