Ronchetti, Elvezio; Field, Christopher; Blanchard, Wade Robust linear model selection by cross-validation. (English) Zbl 1067.62551 J. Am. Stat. Assoc. 92, No. 439, 1017-1023 (1997). Summary: 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 of 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. Cited in 39 Documents MSC: 62J05 Linear regression; mixed models 62F35 Robustness and adaptive procedures (parametric inference) PDFBibTeX XMLCite \textit{E. Ronchetti} et al., J. Am. Stat. Assoc. 92, No. 439, 1017--1023 (1997; Zbl 1067.62551) Full Text: DOI