论文标题
较稀疏的腐败和模型选择的强劲套索零零
Robust Lasso-Zero for sparse corruption and model selection with missing covariates
论文作者
论文摘要
我们提出了强大的套索零，这是最初针对稀疏线性模型引入的拉索 - 零方法的扩展，以解决稀疏的损坏问题。我们对参数恢复的理论保证略有简化的估算器，称为阈值正义追求。展示了可变零零的使用，以用于变量选择，并在协变量中缺少值。除了不需要为协变量的模型规范，或估算其协方差矩阵或噪声方差外，该方法还具有很大的优点，即不指定参数模型而无需随机值。数值实验和医疗应用在这种情况下与少数可用竞争对手的情况下强劲的套索零相关性。该方法易于使用和在R库LASS0中实现。
We propose Robust Lasso-Zero, an extension of the Lasso-Zero methodology, initially introduced for sparse linear models, to the sparse corruptions problem. We give theoretical guarantees on the sign recovery of the parameters for a slightly simplified version of the estimator, called Thresholded Justice Pursuit. The use of Robust Lasso-Zero is showcased for variable selection with missing values in the covariates. In addition to not requiring the specification of a model for the covariates, nor estimating their covariance matrix or the noise variance, the method has the great advantage of handling missing not-at random values without specifying a parametric model. Numerical experiments and a medical application underline the relevance of Robust Lasso-Zero in such a context with few available competitors. The method is easy to use and implemented in the R library lass0.
