Evaluating methods for Lasso selective inference in biomedical research: a comparative simulation study.

ABSTRACT:

Background

Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and replicability issues.

Methods

We compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso: sample splitting, selective inference conditional on the Lasso selection (SI), and universally valid post-selection inference (PoSI). We studied the properties of the proposed selective confidence intervals available via R software packages using a neutral simulation study inspired by real data commonly seen in biomedical s

SUBMITTER: Kammer M 

PROVIDER: S-EPMC9316707 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature