# COBRA: A Combined Regression Strategy

* Auteur correspondant
4 CLASSIC - Computational Learning, Aggregation, Supervised Statistical, Inference, and Classification
DMA - Département de Mathématiques et Applications - ENS Paris, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt
Abstract : A new method for combining several initial estimators of the regression function is introduced. Instead of building a linear or convex optimized combination over a collection of basic estimators $r_1,\dots,r_M$, we use them as a collective indicator of the proximity between the training data and a test observation. This local distance approach is model-free and very fast. More specifically, the resulting nonparametric/nonlinear combined estimator is shown to perform asymptotically at least as well in the sense as the best combination of the basic estimators in the collective. A companion R package called COBRA (standing for COmBined Regression Alternative) is presented (downloadable on http://cran.r-project.org/web/packages/COBRA/index.html). Substantial numerical evidence is provided on both synthetic and real data sets to assess the excellent performance and velocity of our method in a large variety of prediction problems.
Keywords :
Type de document :
Article dans une revue
Journal of Multivariate Analysis, Elsevier, 2016, 〈10.1016/j.jmva.2015.04.007〉
Domaine :
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01361789
Contributeur : Benjamin Guedj <>
Soumis le : mercredi 20 novembre 2013 - 13:33:09
Dernière modification le : vendredi 4 janvier 2019 - 17:32:32

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### Citation

Gérard Biau, Aurélie Fischer, Benjamin Guedj, James Malley. COBRA: A Combined Regression Strategy. Journal of Multivariate Analysis, Elsevier, 2016, 〈10.1016/j.jmva.2015.04.007〉. 〈hal-01361789v2〉

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