A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models
提出一种名为OCMT的变量选择方法，逐个检验协变量的统计显著性并控制多重检验错误率，适用于高维线性回归，计算快、小样本表现好，并用于预测美国产出增长和通胀。
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 Multiple Testing (OCMT) procedure, and use ideas from the multiple testing literature to control the probability of selecting the approximating model, the false positive rate, and the false discovery rate. OCMT is easy to interpret, relates to classical statistical analysis, is valid under general assumptions, is faster to compute, and performs well in small samples. The usefulness of OCMT is also illustrated by an empirical application to forecasting U.S. output growth and inflation.