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Journal Articles Journal of Multivariate Analysis Year : 2016

COBRA: A Combined Regression Strategy


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 $L^2$ 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 \url{}). 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.
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Dates and versions

hal-01361789 , version 2 (20-11-2013)
hal-01361789 , version 1 (07-09-2016)
hal-01361789 , version 3 (23-05-2019)



Gérard Biau, Aurélie Fischer, Benjamin Guedj, James Malley. COBRA: A Combined Regression Strategy. Journal of Multivariate Analysis, 2016, ⟨10.1016/j.jmva.2015.04.007⟩. ⟨hal-01361789v3⟩
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