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.

翻译：在高维回归模型中,要列入预测器的候选共变变量数量相当大,而变量选择也至关重要。 在这项工作中,我们建议一种新的处罚,既能保证选择的变量稀少,即精确的零回归系数估计值,又能保证“选定”变量在高维回归模型中的系数的准不偏差。 模拟结果表明,我们的提案的表现不比其竞争对手差,同时始终确保解决方案的独特性。