A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models
高维线性回归变量选择的逐协变量多重检验方法
This paper provides an alternative approach to penalized regression for model selection in the context of high‐dimensional linear regressions where the number of covariates is large, often much larger than the number of available observations. We consider the statistical significance of individual covariates one at a time, while taking full account of the multiple testing nature of the inferential problem involved. We refer to the proposed method as One Covariate at a Time…