COBRA: A Combined Regression Strategy

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 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.
Type de document :
Article dans une revue
Journal of Multivariate Analysis, Elsevier, 2016, 〈10.1016/j.jmva.2015.04.007〉
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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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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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