# Regression Shrinkage and Selection via the Lasso

@article{Tibshirani1996RegressionSA, title={Regression Shrinkage and Selection via the Lasso}, author={Robert Tibshirani}, journal={Journal of the royal statistical society series b-methodological}, year={1996}, volume={58}, pages={267-288} }

SUMMARY 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. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretableā¦Ā

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