Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso
通过 LAD-lasso 实现稳健回归收缩与一致变量选择
The least absolute deviation (LAD) regression is a useful method for robust regression, and the least absolute shrinkage and selection operator (lasso) is a popular choice for shrinkage estimation and variable selection. In this article we combine these two classical ideas together to produce LAD-lasso. Compared with the LAD regression, LAD-lasso can do parameter estimation and variable selection simultaneously. Compared with the traditional lasso, LAD-lasso is resistant to…