Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.jmva.2015.04.007 | Journal of Multivariate Analysis |
Keywords | DocType | Volume |
62G05,62G20 | Journal | 146 |
ISSN | Citations | PageRank |
0047-259X | 3 | 0.60 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gérard Biau | 1 | 256 | 24.07 |
| Aurélie Fischer | 2 | 3 | 0.60 |
| Benjamin Guedj | 3 | 9 | 8.82 |
| James D. Malley | 4 | 118 | 14.43 |