# Model selection by resampling penalization

Abstract : We present a new family of model selection algorithms based on the resampling heuristics. It can be used in several frameworks, do not require any knowledge about the unknown law of the data, and may be seen as a generalization of local Rademacher complexities and $V$-fold cross-validation. In the case example of least-square regression on histograms, we prove oracle inequalities, and that these algorithms are naturally adaptive to both the smoothness of the regression function and the variability of the noise level. Then, interpretating $V$-fold cross-validation in terms of penalization, we enlighten the question of choosing $V$. Finally, a simulation study illustrates the strength of resampling penalization algorithms against some classical ones, in particular with heteroscedastic data.
Keywords :
Type de document :
Pré-publication, Document de travail
2007
Domaine :
Liste complète des métadonnées

Littérature citée [21 références]

https://hal.archives-ouvertes.fr/hal-00125455
Contributeur : Sylvain Arlot <>
Soumis le : lundi 22 janvier 2007 - 11:41:45
Dernière modification le : jeudi 11 janvier 2018 - 06:12:18
Document(s) archivé(s) le : mardi 21 septembre 2010 - 12:14:26

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colt_court.pdf
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### Citation

Sylvain Arlot. Model selection by resampling penalization. 2007. 〈hal-00125455v2〉

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