Robust estimators under a semiparametric partly linear autoregression: asymptotic behavior and bandwidth selection
In this article, under a semi-parametric partly linear autoregression model, a family of robust estimators for the autoregression parameter and the autoregression function is studied. The proposed estimators are based on a three-step procedure, in which robust regression estimators and robust smooth...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2007 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/125432 |
| Acceso en línea: | http://hdl.handle.net/11336/125432 |
| Access Level: | acceso abierto |
| Palabra clave: | ASYMPTOTIC PROPERTIES CROSS-VALIDATION FILTERING PARTLY LINEAR AUTOREGRESSION PREDICTION RATE OF CONVERGENCE ROBUST ESTIMATION SMOOTHING TECHNIQUES https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Sumario: | In this article, under a semi-parametric partly linear autoregression model, a family of robust estimators for the autoregression parameter and the autoregression function is studied. The proposed estimators are based on a three-step procedure, in which robust regression estimators and robust smoothing techniques are combined. Asymptotic results on the autoregression estimators are derived. Besides combining robust procedures with M-smoothers, predicted values for the series and detection residuals, which allow to detect anomalous data, are introduced. Robust cross-validation methods to select the smoothing parameter are presented as an alternative to the classical ones, which are sensitive to outlying observations. A Monte Carlo study is conducted to compare the performance of the proposed criteria. Finally, the asymptotic distribution of the autoregression parameter estimator is stated uniformly over the smoothing parameter. |
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