COBRA: A combined regression strategy. 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 r 1 ,...,r M , 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 L 2 sense as the best combination of the basic estimators in the collective. A companion R package called (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.
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References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Biau, G.; Cadre, B.; Rouvière, L.: Accelerated gradient boosting (2019)
- Fischer, Aurélie; Mougeot, Mathilde: Aggregation using input-output trade-off (2019)
- Guedj, Benjamin; Desikan, Bhargav Srinivasa: Pycobra: a Python toolbox for ensemble learning and visualisation (2018)
- Biau, Gérard; Fischer, Aurélie; Guedj, Benjamin; Malley, James D.: COBRA: a combined regression strategy (2016)
- Cholaquidis, Alejandro; Fraiman, Ricardo; Kalemkerian, Juan; Llop, Pamela: A nonlinear aggregation type classifier (2016)
- Goia, Aldo (ed.); Vieu, Philippe (ed.): An introduction to recent advances in high/infinite dimensional statistics (2016)
- Mojirsheibani, Majid; Kong, Jiajie: An asymptotically optimal kernel combined classifier (2016)