Indicator Selection of Index Construction by Adaptive Lasso with a Generic epsilon-Insensitive Loss
Indicator Selection of Index Construction by Adaptive Lasso with a Generic epsilon-Insensitive Loss
复制标题
具有通用 epsilon 不敏感损失的自适应 Lasso 索引构建的指标选择
DOI：
10.1007/s10614-021-10175-w
复制
发表时间：
2021
影响因子：
2
通讯作者：
Hua Xiangyu
中科院分区：
文献类型：
--
作者：
Ye Yafen;Chi Renyong;Shao Yuan-Hai;Li Chun-Na;Hua Xiangyu
成功的指数有助于决策者确定基准业绩和趋势，并确定政策优先事项。从系统中大量潜在的候选变量中选取具有代表性的变量是指数构建成功的关键。一个强大的和稀疏的方法反映了一个迫切需要有效地选择有用的变量指数建设。虽然最小绝对收缩和选择算子（Lasso）是一种流行的变量选择技术，但它会受到离群值或噪声的影响。在本文中，我们提出了一个强大的Lasso与通用的不敏感和自适应损失函数（GIA-Lasso）的变量选择。通用损失函数可以通过调整不敏感参数、弹性区间参数和自适应鲁棒化参数来实现对离群值的极大鲁棒性。GIA-Lasso中的-范数正则化项和监督选择过程确保选择最具代表性的变量。金融状况指数和创新创业指数的变量选择结果证实了GIA-Lasso不仅对异常值具有稳健性，而且选择了具有代表性的变量。格兰杰因果关系检验进一步证明了所选变量的合理性。
A successful index helps policy decision makers identify benchmark performances and trends and set policy priorities. Selecting representative variables from a large number of potential candidates in the system is crucial to the success of index construction. A robust and sparse method reflects an urgent need to effectively select useful variables for index construction. Although the least absolute shrinkage and selection operator (Lasso) is a popular technique for variable selection, it suffers from the influence of outlier or noise. In this paper, we propose a robust Lasso with a generic insensitive and adaptive loss function (GIA-Lasso) for variable selection. The generic loss function can achieve great robustness against outliers by adjusting an insensitive parameter, an elastic interval parameter, and an adaptive robustification parameter. The-norm regularization term and the supervised selection process in GIA-Lasso ensure that the most representative variables are selected. The variable selection results of the Financial Conditions Index and Innovation and Entrepreneurship Index confirm that GIA-Lasso is not only robust against outliers, but also selects representative variables. The Granger causality test further proves the reasonability of the selected variables.