The title Lasso has been suggested by Tibshirani 6] as colourful name for a technique for variable selection which requires the minimization of a sum of squares subject to an l 1 bound on the solution. This bound then has the role of the selection parameter. Here a descent method for solving the constrained problem is formulated, a homotopy method in which the constraint bound becomes the homotopy parameter is developed to completely describe the possible selection regimes, and it is suggested that modi ed Gram-Schmidt applied to the augmented design matrix provides an e ective base for implementing the suggested algorithms.

Proposed by Tibshirani, the least absolute shrinkage and selection operator (LASSO) estimates a vector of regression coefficients by minimizing the residual sum of squares subject to a constraint on the l'-norm of the coefficient vector. The LASSO estimator typically has one or more zero elements and thus shares characteristics of both shrinkage estimation and variable selection. In this article we treat the LASSO as a convex programming problem and derive its dual. Consideration of the primal and dual problems together leads to important new insights into the characteristics of the LASSO estimator and to an improved method for estimating its covariance matrix. Using these results we also develop an efficient algorithm for computing LASSO estimates which is usable even in cases where the number of regressors exceeds the number of observations. An S-Plus library based on this algorithm is available from StatLib.

Proposed by Tibshirani (1996), the LASSO (least absolute shrinkage and selection operator) estimates a vector of regression coefficients by minimising the residual sum of squares subject to a constraint on the l 1-norm of coefficient vector. The LASSO estimator typically has one or more zero elements and thus shares characteristics of both shrinkage estimation and variable selection. In this paper we treat the LASSO as a convex programming problem and derive its dual. Consideration of the primal and dual problems together leads to important new insights into the characteristics of the LASSO estimator and to an improved method for estimating its covariance matrix. Using these results we also develop an efficient algorithm for computing LASSO estimates which is usable even in cases where the number of regressors exceeds the number of observations.

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