COBRA: A combined regression strategy

Gérard Biau, Aurélie Fischer, Benjamin Guedj and James D. Malley

Journal of Multivariate Analysis, 2016, vol. 146, issue C, 18-28

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 r1,…,rM, 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 L2 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: Combining estimators; Consistency; Nonlinearity; Nonparametric regression; Prediction (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2015.04.007

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