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| HAL : hal-00184869, version 1 |
| arXiv : 0711.0372 |
| Fiche détaillée |  Récupérer au format |
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| Mixing Least-Squares Estimators when the Variance is Unknown |
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| Christophe Giraud 1 |
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| (03/02/2007) |
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| We propose a procedure to handle the problem of Gaussian regression when the variance is unknown. We mix least-squares estimators from various models according to a procedure inspired by that of Leung and Barron (2007). We show that in some cases the resulting estimator is a simple shrinkage estimator. We then apply this procedure in various statistical settings such as linear regression or adaptive estimation in Besov spaces. Our results provide non-asymptotic risk bounds for the Euclidean risk of the estimator. |
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| 1 : | Laboratoire Jean Alexandre Dieudonné (JAD) |
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CNRS : UMR6621 – Université de Nice Sophia Antipolis (UNS) |
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| Domaine | : | Mathématiques/Statistiques Statistiques/Théorie |
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| Gibbs mixture – shrinkage estimator – oracle inequalities – adaptive estimation – linear regression |
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| Liste des fichiers attachés à ce document : | ||||||||||
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| hal-00184869, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00184869 | |
| oai:hal.archives-ouvertes.fr:hal-00184869 | |
| Contributeur : Christophe Giraud | |
| Soumis le : Vendredi 2 Novembre 2007, 14:38:01 | |
| Dernière modification le : Vendredi 2 Novembre 2007, 19:36:43 | |
