Lasso for linear models

Abstract

The Lasso, proposed by Tibshirani (1996), is an acronym for Least Absolute Shrinkage and Selection Operator. Among the main reasons why it has become very popular for high-dimensional estimation problems are its statistical accuracy for prediction and variable selection coupled with its computational feasibility. Furthermore, since the Lasso is a penalized likelihood approach, the method is rather general and can be used in a broad variety of models. In the simple case of a linear model with orthonormal design, the Lasso equals the soft thresholding estimator introduced and analyzed by Donoho and Johnstone (1994). The Lasso for linear models is the core example to develop the methodology for ℓ₁-penalization in highdimensional settings. We discuss in this chapter some fundamental methodological and computational aspects of the Lasso. We also present the adaptive Lasso, an important two-stage procedure which addresses some bias problems of the Lasso. The methodological steps are supported by describing various theoretical results which will be fully developed in Chapters 6 and 7.