Influence analysis of robust Wald-type tests
We consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based on the minimum density power divergence estimators instead of the maximum likelihood estimators. An extensive study of th...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/24512 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/24512 |
| Access Level: | acceso abierto |
| Palabra clave: | 519.22 Divergence measures Wald-type test statistics Minimum density power divergence estimators Robustness Influence functions Chi-square inflation factor Estadística matemática (Matemáticas) 1209 Estadística |
| Sumario: | We consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based on the minimum density power divergence estimators instead of the maximum likelihood estimators. An extensive study of their robustness properties is given though the influence functions as well as the chi-square inflation factors. It is theoretically established that the level and power of these robust tests are stable against outliers, whereas the classical Wald test breaks down. Some numerical examples confirm the validity of the theoretical results. |
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