We propose a new method for estimation in linear models. The `lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant.

The application of the lasso is espoused in high dimensional settings where only a small number of the regression coefficients are believed to be non-zero (i.e. the solution is sparse). Moreover, ...