# COBRA: A Combined Regression Strategy

arXiv: https://arxiv.org/abs/1303.2236 · HTML: https://mlsanity.com/paper/1303.2236/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2013.

## Built on by (most cited first, 6 of 6)

- [Concordance based Survival Cobra with regression type weak learners](https://mlsanity.com/paper/2209.11919/) (2022)
- [Model-independent variable selection via the rule-based variable priority](https://mlsanity.com/paper/2409.09003/) (2024)
- [GROS: A General Robust Aggregation Strategy](https://mlsanity.com/paper/2402.15442/) (2024)
- [Consensual Aggregation on Random Projected High-dimensional Features for Regression](https://mlsanity.com/paper/2204.02606/) (2022)
- [Controlling Travel Path of Original Cobra](https://mlsanity.com/paper/2210.10655/) (2022)
- [Some variation of COBRA in sequential learning setup](https://mlsanity.com/paper/2405.04539/) (2024)

## Abstract (arXiv)

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,dots,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 urlhttp://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.