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 , 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 \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.
Cite
@article{arxiv.1303.2236,
title = {COBRA: A Combined Regression Strategy},
author = {Gérard Biau and Aurélie Fischer and Benjamin Guedj and James Malley},
journal= {arXiv preprint arXiv:1303.2236},
year = {2019}
}
Comments
42 pages