Detecting Interactions in High-Dimensional Data Using Cross Leverage Scores.

ABSTRACT: We develop a variable selection method for interactions in regression models on large data in the context of genetics. The method is intended for investigating the influence of single-nucleotide polymorphisms (SNPs) and their interactions on health outcomes, which is a p ≫ n $p\gg n$ problem. We introduce cross leverage scores (CLSs) to detect interactions of variables while maintaining interpretability. Using this method, it is not necessary to consider every possible interaction between variables individually, which would be very time-consuming even for moderate amounts of variables. Instead, we calculate the CLS for each variable and obtain a measure of importance for this variable. Calculating the scores remains time-consuming for large data sets. The key idea for scaling to large da

SUBMITTER: Teschke S 

PROVIDER: S-EPMC11605674 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature