Robust linear model selection by cross-validation
Robust linear model selection by cross-validation
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DOI：
10.2307/2965566
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发表时间：
1997-09-01
影响因子：
3.7
通讯作者：
Blanchard, W
中科院分区：
文献类型：
--
作者：
Ronchetti, E;Field, C;Blanchard, W
本文给出了回归模型中模型选择的鲁棒技术，这是涉及回归的任何数据分析的一个重要方面。异常值有可能对所选模型产生不适当的影响，并扭曲任何后续分析。我们提供了一个强大的算法模型选择使用邵的交叉验证方法作为起点的变量的选择。因为Shao的技术是基于最小二乘法的，所以它们对异常值很敏感。我们开发我们的强大的过程中使用相同的想法，交叉验证邵，但使用的估计是最佳的有界影响的预测。我们证明了我们的强大的程序在提供保护，防止离群值在模拟研究和真实的例子中的有效性。我们将结果与Shao的方法进行了对比，证明了在正常模型的效率损失很小的情况下，在存在离群值的情况下选择正确模型的实质性改进。
This article gives a robust technique for model selection in regression models, an important aspect of any data analysis involving regression. There is a danger that outliers will have an undue influence on the model chosen and distort any subsequent analysis. We provide a robust algorithm for model selection using Shao's cross-validation methods for choice of variables as a starting point. Because Shao's techniques are based on least squares, they are sensitive to outliers. We develop our robust procedure using the same ideas of cross-validation as Shao but using estimators that are optimal bounded influence for prediction. We demonstrate the effectiveness of our robust procedure in providing protection against outliers both in a simulation study and in a real example. We contrast the results with those obtained by Shao's method, demonstrating a substantial improvement in choosing the correct model in the presence of outliers with little loss of efficiency at the normal model.