A Generalized Dantzig Selector with Shrinkage Tuning

نویسنده

  • PETER RADCHENKO
چکیده

The Dantzig selector performs variable selection and model fitting in linear regression. It uses an L1 penalty to shrink the regression coefficients towards zero, in a similar fashion to the Lasso. While both the Lasso and Dantzig selector potentially do a good job of selecting the correct variables, they tend to over-shrink the final coefficients. This results in an unfortunate trade-off. One can either select a high shrinkage tuning parameter that produces an accurate model but poor coefficient estimates or a low shrinkage parameter that produces more accurate coefficients but includes many irrelevant variables. We extend the Dantzig selector to fit generalized linear models while also eliminating over-shrinkage of the coefficient estimates. In addition, we develop a computationally efficient algorithm, similar in nature to least angle regression, to compute the entire path of coefficient estimates. A detailed simulation study illustrates the advantages of our approach relative to several other possible methods. Finally, we apply the methodology to two real-world datasets.

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تاریخ انتشار 2008