Regression shrinkage and selection via the Lasso
Regression shrinkage and selection via the Lasso
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DOI：
10.1111/j.2517-6161.1996.tb02080.x
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发表时间：
1996-01-01
影响因子：
5.8
通讯作者：
Tibshirani, R
中科院分区：
文献类型：
--
作者：
Tibshirani, R
提出了一种新的线性模型参数估计方法。"套索“使残差平方和最小化，前提是系数的绝对值之和小于常数。由于这种约束的性质，它往往会产生一些恰好为0的系数，因此给出了可解释的模型。我们的模拟研究表明，套索具有子集选择和岭回归的一些有利特性。它产生可解释的模型，如子集选择，并表现出岭回归的稳定性。也有一个有趣的关系，最近的工作在自适应函数估计多诺霍和约翰斯通。套索的思想是相当普遍的，可以应用于各种统计模型：广义回归模型和基于树的模型的扩展进行了简要描述。
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 interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. 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.