Model-free Feature Screening via Revised Chatterjee's Rank Correlation for Ultra-high Dimensional Censored Data

TL;DR

This paper proposes a simple, robust feature screening method for ultra-high dimensional censored survival data using a modified Chatterjee's rank correlation, with proven theoretical properties and demonstrated superior performance.

Contribution

It introduces a novel, easy-to-compute feature screening technique applicable to various survival models, with theoretical guarantees and empirical validation.

Findings

Method achieves sure screening and ranking consistency.

Simulation studies show superior efficacy over existing methods.

Real gene expression data analysis confirms practical usefulness.

Abstract

In large-scale biomedical research, it's common to gather ultra-high dimensional data that includes right-censored survival times. Feature screening has emerged as a crucial statistical technique for handling such data. In this paper, we introduce a straightforward and robust feature screening approach, leveraging the modified Chatterjee's rank correlation, suitable for a broad range of survival models. With reasonably mild regularity assumptions, we establish the properties of sure screening and ranking consistency. The computation involved in our proposed method is quite direct and simple. Through simulation studies and real gene expression data analysis, we demonstrate the superior efficacy of our proposed approach.