Inference for sparse linear regression based on the leave-one-covariate-out solution path.

ABSTRACT: 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 coe cient 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 achiev

SUBMITTER: Cao X 

PROVIDER: S-EPMC10572792 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature