Robust Linear Model Selection by Cross-Validation
本文提出一种稳健的回归模型选择方法，基于Shao的交叉验证但改用有界影响估计量，在存在异常值时能更准确地选择正确模型，且对正常模型效率损失小。
Abstract 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.