论文标题
CDF惩罚：回归模型中的稀疏和准公正估计
The CDF penalty:sparse and quasi unbiased estimation in regression models
论文作者
论文摘要
在高维回归模型中，预测器中包含的候选协变量数量很大，并且可变选择至关重要。在这项工作中，我们提出了一种新的惩罚，能够保证稀疏变量选择，即准确的回归系数估计值，以及对高维回归模型中“选定”变量系数的准稳态。仿真结果表明，我们的提案的表现并不比其竞争对手差，同时始终确保解决方案是唯一的。
In high-dimensional regression modelling, the number of candidate covariates to be included in the predictor is quite large, and variable selection is crucial. In this work, we propose a new penalty able to guarantee both sparse variable selection, i.e. exactly zero regression coefficient estimates, and quasi-unbiasedness for the coefficients of 'selected' variables in high dimensional regression models. Simulation results suggest that our proposal performs no worse than its competitors while always ensuring that the solution is unique.
