Prior Knowledge Guided Ultra-high Dimensional Variable Screening with Application to Neuroimaging Data.

ABSTRACT: Variable screening is a powerful and efficient tool for dimension reduction under ultrahigh dimensional settings. However, most existing methods overlook useful prior knowledge in specific applications. In this work, from a Bayesian modeling perspective, we develop a unified variable screening procedure for the linear regression model. We discuss different constructions of posterior mean screening (PMS) statistics to incorporate different types of prior knowledge according to specific applications. With non-informative prior specifications, PMS is equivalent to high-dimensional ordinary least-square projections (HOLP). We establish the screening consistency property for PMS with different types of prior knowledge. We show that PMS is robust to prior misspecifications; and when the prior kn

SUBMITTER: He J 

PROVIDER: S-EPMC9426412 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature