Interpretable High-Dimensional Inference Via Score Projection with an Application in Neuroimaging.

ABSTRACT: In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the associations between the outcome and summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify regions associated with the outcome. Here, we propose a generalization of Rao's score test based on projecting the score statistic onto a linear subspace of a high-dimensional parameter space. The approach provides a way to localize signal in the high-dimensional space by projecting the scores to the subspace where the score

SUBMITTER: Vandekar SN 

PROVIDER: S-EPMC6756796 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature