A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology.

ABSTRACT: The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational and high-dimensional biology. As the dimensionality of datasets continues to grow, so too does the complexity of identifying biomarkers linked to exposure patterns. The statistical analysis of such data often relies upon parametric modeling assumptions motivated by convenience, inviting opportunities for model misspecification. While estimation frameworks incorporating flexible, data adaptive regression strategies can mitigate this, their standard variance estimators are often unstable in high-dimensional settings, resulting in inflated Type-I error even after standard multiple testing corrections. We adapt a shrinkage app

SUBMITTER: Hejazi NS 

PROVIDER: S-EPMC11078029 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature