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Discussion of “least angle regression” by Efron et al

By A. Stine


I have enjoyed reading the work of each of these authors over the years, so it is a real pleasure to have this opportunity to contribute to the discussion of this collaboration. The geometry of LARS furnishes an elegant bridge between the Lasso and Stagewise regression, methods that I would not have suspected to be so related. Toward my own interests, LARS offers a rather different way to construct a regression model by gradually blending predictors rather than using a predictor all at once. I feel that the problem of “automatic feature generation ” (proposing predictors to consider in a model) is a current challenge in building regression models that can compete with those from computer science, and LARS suggests a new approach to this task. In the examples of Efron, Hastie, Johnstone and Tibshirani (EHJT) (particularly that summarized in their Figure 5), LARS produces models with smaller predictive error than the old workhorse, stepwise regression. Furthermore, as an added bonus, the code supplied by the authors runs faster for me than the step routine for stepwise regression supplie

Year: 2004
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