Abstract

Identifying outcome-related variables is of general research interest in biomedical research. This

task can be complicated by the presence of dynamic (or varying) variable effects that often manifest meaningful scientific mechanisms. Appropriately accounting for possible dynamic effects is crucial to avoid

depreciating some important variables. In this work, we propose a model-free testing and screening framework by adopting a global view pertaining to the concept of interval quantile independence. The new

framework not only permits robust identification of variables dynamically associated with an outcome, but

also offers the flexibility to perform group testing that simultaneously evaluates multiple continuous or

discrete covariates. We show that the key testing strategy can naturally evolve into unconditional and conditional screening procedures for ultra-high dimensional settings that enjoys the desirable sure screening

property. We demonstrate good practical utility of the proposed methods via extensive simulation studies

and a real application to a microarray data set.

Information

Preprint No.SS-2023-0285
Manuscript IDSS-2023-0285
Complete AuthorsYing Cui, Limin Peng
Corresponding AuthorsLimin Peng
Emailslpeng@emory.edu

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Acknowledgments

This work was supported by NIH grant R01 HL113548.

Supplementary Materials

available online includes technical proofs and additional results.

Supplementary materials are available for download.