Robust Wald-type tests based on minimum Rényi pseudodistance estimators for the multiple linear regression model
We introduce a new family of Wald-type tests, based on minimum Rényi pseudodistance estimators, for testing general linear hypotheses and the variance of the residuals in the multiple regression model. The classical Wald test, based on the maximum likelihood estimator, can be seen as a particular ca...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/7239 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/7239 |
| Access Level: | acceso abierto |
| Palabra clave: | 311 51 Influence function Minimum density power divergence estimator Multiple regresion model Rény pseudodistance Robustness regression model Rényi Pseudodistance Robustness Matemáticas (Matemáticas) Estadística 12 Matemáticas 1209 Estadística |
| Sumario: | We introduce a new family of Wald-type tests, based on minimum Rényi pseudodistance estimators, for testing general linear hypotheses and the variance of the residuals in the multiple regression model. The classical Wald test, based on the maximum likelihood estimator, can be seen as a particular case inside our family. Theoretical results, supported by an extensive simulation study, point out how some tests included in this family have a better behaviour, in the sense of robustness, than the Wald test. Finally, we provide a data-driven procedure for the choice of the optimal test given any data set. |
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