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L 1 regularization, or regularization with an L 1 penalty, is a popular idea in statistics and machine learning. This paper reviews the concept and application of L 1 regularization for regression. It is not our aim to present a comprehensive list of the utilities of the L 1 penalty in the regression setting. Rather, we focus on what we believe is the set of most representative uses of this regularization technique, which we describe in some detail. Thus, we deal with a number of L 1 -regularized methods for linear regression, generalized linear models, and time series analysis. Although this review targets practice rather than theory, we do give some theoretical details about L 1 -penalized linear regression, usually referred to as the least absolute shrinkage and selection operator (lasso). © 2013 International Statistical Institute.

Original publication




Journal article


International Statistical Review

Publication Date





361 - 387