Integrative analysis of high-dimensional quantile regression with contrasted penalization.

ABSTRACT: In the era of big data, the simultaneous analysis of multiple high-dimensional, heavy-tailed datasets has become essential. Integrative analysis offers a powerful approach to combine and synthesize information from these various datasets, and often outperforming traditional meta-analysis and single-dataset analysis. In this paper, we introduce a novel high-dimensional integrative quantile regression that can accommodate the complexities inherent in multi-dataset analysis. A contrast penalty that smooths regression coefficients is introduced to account for across-dataset structures and improve variable selection. To ease the computational burden associated with high-dimensional quantile regression, a new algorithm is developed that is effective at computing solution paths and selecting sign

SUBMITTER: Ren P 

PROVIDER: S-EPMC12217111 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature