论文标题
基于剩下的一体式套索路径的高维推断
High-Dimensional Inference Based on the Leave-One-Covariate-Out LASSO Path
论文作者
论文摘要
我们提出了一个新的量度，该量度是基于套管省略时套索解决方案路径的变化在高维回归中的新量度。提出的程序提供了一种计算可变重要性和进行可变筛选的新方法。此外，我们的程序允许构建p值，以测试每个系数是否等于零以及同时测试涉及多个回归系数的假设；引导技术用于构建零分布。对于低维线性模型，我们的方法可以实现比$ t $检验更高的功率。提供了广泛的模拟以显示我们方法的有效性。在高维环境中，我们提出的基于解决方案路径的测试具有比最近开发的高维推理方法更大的功率。
We propose a new measure of variable importance in high-dimensional regression based on the change in the LASSO solution path when one covariate is left out. The proposed procedure provides a novel way to calculate variable importance and conduct variable screening. In addition, our procedure allows for the construction of P-values for testing whether each coefficient is equal to zero as well as for testing hypotheses involving multiple regression coefficients simultaneously; bootstrap techniques are used to construct the null distribution. For low-dimensional linear models, our method can achieve higher power than the $t$-test. Extensive simulations are provided to show the effectiveness of our method. In the high-dimensional setting, our proposed solution path based test achieves greater power than some other recently developed high-dimensional inference methods.
