COBRA: A Combined Regression Strategy
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Mar 2013.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 3.
6 papers.
- 2022Concordance based Survival Cobra with regression type weak learnerscited by 2
- 2024Model-independent variable selection via the rule-based variable prioritycited by 2
- 2024GROS: A General Robust Aggregation Strategycited by 1
- 2022Consensual Aggregation on Random Projected High-dimensional Features for Regressioncited by 0
- 2022Controlling Travel Path of Original Cobracited by 0
- 2024Some variation of COBRA in sequential learning setupcited by 0
- 2024Model-independent variable selection via the rule-based variable priority13 Sep 2024
- 2024Some variation of COBRA in sequential learning setup7 Apr 2024
- 2024GROS: A General Robust Aggregation Strategy23 Feb 2024
- 2022Controlling Travel Path of Original Cobra15 Oct 2022
- 2022Concordance based Survival Cobra with regression type weak learners24 Sep 2022
- 2022Consensual Aggregation on Random Projected High-dimensional Features for Regression6 Apr 2022