Sparsity Oriented Importance Learning for High-Dimensional Linear Regression
高维线性回归中面向稀疏性的变量重要性学习
With now well-recognized nonnegligible model selection uncertainty, data analysts should no longer be satisfied with the output of a single final model from a model selection process, regardless of its sophistication. To improve reliability and reproducibility in model choice, one constructive approach is to make good use of a sound variable importance measure. Although interesting importance measures are available and increasingly used in data analysis, little theoretical…