Regression Shrinkage and Selection Via the Lasso

We propose a new method for estimation in linear models

Robert Tibshirani

2018

Scholarcy highlights

  • 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
  • Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression
  • The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described

Need more features? Save interactive summary cards to your Scholarcy Library.