Targeted Undersmoothing: Sensitivity Analysis for Sparse Estimators
提出一种名为目标欠平滑的方法，用于评估稀疏高维模型在模型选择后对推断结论的敏感性，通过系统扩大初始选定模型来检验实证结论对模型选择错误的稳健性。
Abstract This paper proposes a procedure for assessing the sensitivity of inferential conclusions for functionals of sparse high-dimensional models following model selection. The proposed procedure is called targeted undersmoothing. Functionals considered include dense functionals that may depend on many or all elements of the high-dimensional parameter vector. The sensitivity analysis is based on systematic enlargements of an initially selected model. By varying the enlargements, one can conduct sensitivity analysis about the strength of empirical conclusions to model selection mistakes. We illustrate the procedure's performance through simulation experiments and two empirical examples.