LASSO tuning parameter selection
LASSO tuning parameter selection
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发表时间：
2015
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通讯作者：
Lisa-Ann Kirkland;F. Kanfer;S. Millard
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作者：
Lisa-Ann Kirkland;F. Kanfer;S. Millard
LASSO是一种惩罚回归方法，它同时执行收缩和变量选择。由LASSO产生的输出由分段线性解路径组成，随着调谐参数的值的减小，从零模型开始，以全最小二乘拟合结束。因此，所选模型的性能在很大程度上取决于该参数的选择。本文试图提供一个概述的方法，可用于选择值的调谐参数的预测或变量选择的目的。模拟研究提供了这些方法的比较，并评估其性能。
The LASSO is a penalized regression method which simultaneously performs shrinkage and variable selection. The output produced by the LASSO consists of a piecewise linear solution path, starting with the null model and ending with the full least squares fit, as the value of a tuning parameter is decreased. The performance of the selected model therefore depends greatly on the choice of this parameter. This paper attempts to provide an overview of methods which are available to select the value of the tuning parameter for either prediction or variable selection purposes. A simulation study provides a comparison of these methods and assesses their performance.