High finite-sample efficiency and robustness based on distance-constrained maximum likelihood
Good robust estimators can be tuned to combine a high breakdown point and a specified asymptotic efficiency at a central model. This happens in regression with MM- and -estimators among others. However, the finite-sample efficiency of these estimators can be much lower than the asymptotic one. To ov...
Guardado en:
| Autores principales: | , |
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| Formato: | Articulo Preprint |
| Lenguaje: | Inglés |
| Publicado: |
2015
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/101650 https://ri.conicet.gov.ar/11336/42723 |
| Aporte de: |
| Sumario: | Good robust estimators can be tuned to combine a high breakdown point and a specified asymptotic efficiency at a central model. This happens in regression with MM- and -estimators among others. However, the finite-sample efficiency of these estimators can be much lower than the asymptotic one. To overcome this drawback, an approach is proposed for parametric models, which is based on a distance between parameters. Given a robust estimator, the proposed one is obtained by maximizing the likelihood under the constraint that the distance is less than a given threshold. For the linear model with normal errors, simulations show that the proposed estimator attains a finite-sample efficiency close to one while improving the robustness of the initial estimator. The same approach also shows good results in the estimation of multivariate location and scatter. |
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