# COBRA: A Nonlinear Aggregation Strategy

* Auteur correspondant
4 CLASSIC - Computational Learning, Aggregation, Supervised Statistical, Inference, and Classification
DMA - Département de Mathématiques et Applications, 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 collective estimator is shown to perform asymptotically at least as well in the $L^2$ sense as the best basic estimator in the collective. Moreover, it does so without having to declare which might be the best basic estimator for the given data set. A companion R package called \cobra (standing for COmBined Regression Alternative) is presented (downloadable on \url{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 :
Pré-publication, Document de travail
40 pages, 5 tables, 12 figures. 2013
Domaine :

https://hal.archives-ouvertes.fr/hal-01361789
Contributeur : Benjamin Guedj <>
Soumis le : mercredi 20 novembre 2013 - 13:33:09
Dernière modification le : jeudi 27 avril 2017 - 09:46:21

### Fichiers

bfgm2013.pdf
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### Identifiants

• HAL Id : hal-01361789, version 2
• ARXIV : 1303.2236

### Citation

Gérard Biau, Aurélie Fischer, Benjamin Guedj, James Malley. COBRA: A Nonlinear Aggregation Strategy. 40 pages, 5 tables, 12 figures. 2013. <hal-01361789v2>

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