COBRA: A combined regression strategy
COBRA: A combined regression strategy
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DOI：
10.1016/j.jmva.2015.04.007
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发表时间：
2016-04-01
影响因子：
1.6
通讯作者：
Malley, James D.
中科院分区：
文献类型：
--
作者：
Biau, Gerard;Fischer, Aurelie;Malley, James D.
本文提出了一种新的组合回归函数初始估计的方法。代替在基本估计量r（1），.的集合上建立线性或凸优化组合，r（m），我们使用它们作为训练数据和测试观察之间的接近度的集体指标。这种局部距离方法是无模型的，并且非常快。更具体地说，所得到的非参数/非线性组合估计的渐近执行至少以及在L-2意义上的最佳组合的基本估计的集体。提供了一个名为COBRA（代表COmBined Regression Alternative）的配套R包（可在http：//cran.r-project.org/web/packages/COBRA/index.html上下载）。合成和真实的数据集上提供了大量的数值证据，以评估我们的方法在各种各样的预测问题中的优异性能和速度。(C)2015 Elsevier Inc. All rights reserved.
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 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. (C) 2015 Elsevier Inc. All rights reserved.