# Iterated Feature Screening based on Distance Correlation for Ultrahigh-Dimensional Censored Data with Covariates Measurement Error

## TL;DR

This paper introduces an iterated feature screening method based on distance correlation designed for ultrahigh-dimensional survival data with covariate measurement error, effectively identifying important variables despite complex data issues.

## Contribution

It proposes a novel iterative feature screening approach that handles both censoring and measurement error in ultrahigh-dimensional survival data, improving variable selection accuracy.

## Key findings

- The method outperforms existing screening techniques in simulations.
- It effectively detects important covariates with complex dependencies.
- Application to real datasets demonstrates practical utility.

## Abstract

Feature screening is an important method to reduce the dimension and capture informative variables in ultrahigh-dimensional data analysis. Many methods have been developed for feature screening. These methods, however, are challenged by complex features pertinent to the data collection as well as the nature of the data themselves. Typically, incomplete response caused by right-censoring and covariates measurement error are often accompanying with survival analysis. Even though there are many methods have been proposed for censored data, little work has been available when both incomplete response and measurement error occur simultaneously. In addition, the conventional feature screening methods may fail to detect the truly important covariates which are marginally independent of the response variable due to correlations among covariates. In this paper, we explore this important problem and propose the valid feature screening method in the presence of survival data with measurement error. In addition, we also develop the iteration method to improve the accuracy of selecting all important covariates. Numerical studies are reported to assess the performance of the proposed method. Finally, we implement the proposed method to two different real datasets.