Osborne, Michael; Prvan, Tania
The lasso algorithm for variable selection in linear models, intro- duced by Tibshirani, works by imposing an l1 norm bound constraint on the variables in a least squares model and then tuning the model estimation calculation using this bound. This introduction of the bound is interpreted as a form of regularisation step. It leads to a form of quadratic program which is solved by a straight-forward modifica-tion of a standard active set algorithm for each value of this bound. Considerable...[Show more]
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