A Model Selection Criterion for LASSO Estimate with Scaling
A Model Selection Criterion for LASSO Estimate with Scaling
复制标题
DOI：
10.1007/978-3-030-36711-4_22
复制
发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
K. Hagiwara
中科院分区：
文献类型：
--
作者：
K. Hagiwara
已经有几个研究来放松LASSO（最小绝对收缩和选择算子）中的偏差问题。本文考虑用尺度法解决LASSO估计的有偏问题，并在此基础上导出了一个模型选择准则。所提出的尺度值能够有效地补偿LASSO估计的过度收缩，并且易于使用LASSO估计进行计算。此外，我们推导出SURE（Stein的无偏风险估计）作为模型选择的标准。该解析解也是所提出的缩放值的益处。此外，我们通过一个简单的数值例子验证了风险估计，并证实了其有效性。
There have been several studies to relax a bias problem in LASSO (Least Absolute Shrinkage and Selection Operator). In this article, we considered to solve a bias problem of LASSO estimator by scaling and derived a model selection criterion under the scaling method. The proposed scaling value is valid to compensate the excessive shrinkage of LASSO estimator and is easy to compute by using LASSO estimator. Moreover, we derived SURE (Stein’s Unbiased Risk Estimate) as a model selection criterion. This analytic solution is also a benefit of the proposed scaling value. Furthermore, we verified the risk estimate and confirmed its effectiveness through a simple numerical example.