This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Open AccessFeature PaperArticle
A Two-Stage Feature Screening Framework for Ultrahigh-Dimensional Survival Data
by
Xinyi Min
Xinyi Min
Wenbo Wu
Wenbo Wu
Dr. Wenbo Wu is the Chair of the Department of Operations and Analytics, Graham Weston Endowed and a [...]
Dr. Wenbo Wu is the Chair of the Department of Operations and Analytics, Graham Weston Endowed Professor and an associate professor in Operations and Analytics at The University of Texas at San Antonio. Before moving to San Antonio, he was an assistant professor at the University of Oregon. He received his PhD in statistics from the University of Georgia in 2015. His active research areas include high-dimensional data modeling and inference, dimension reduction, variable selection, and causal inference. He also collaborates with researchers in other domains such as finance, marketing, engineering, and computer science. He has established a good publication record in high-ranking journals and teaches both undergraduate and graduate-level statistics and data analytics courses.
Baoying Yang
Baoying Yang
1
Department of Statistics, College of Mathematics, Southwest Jiaotong University, Chengdu 611756, China
2
Department of Operations and Analytics, The University of Texas at San Antonio, San Antonio, TX 78249, USA
*
Author to whom correspondence should be addressed.
Mathematics 2026, 14(1), 93; https://doi.org/10.3390/math14010093 (registering DOI)
Submission received: 16 November 2025
/
Revised: 17 December 2025
/
Accepted: 21 December 2025
/
Published: 26 December 2025
Abstract
Identifying important features associated with right-censored survival time in ultrahigh-dimensional survival data is a challenging task due to the curse of dimensionality and information loss caused by censoring. To address these challenges, we propose a two-stage feature screening framework consisting of an imputation step and a feature screening step. The use of Buckley–James imputation leverages information from censoring and can therefore enhance the overall screening performance, particularly when the censoring rate is relatively high. We establish the sure screening properties of the two screening procedures proposed under this framework and illustrate their advantages through simulations. A real-world example is also provided to demonstrate the practical usefulness of the proposed approach.
Share and Cite
MDPI and ACS Style
Min, X.; Wu, W.; Yang, B.
A Two-Stage Feature Screening Framework for Ultrahigh-Dimensional Survival Data. Mathematics 2026, 14, 93.
https://doi.org/10.3390/math14010093
AMA Style
Min X, Wu W, Yang B.
A Two-Stage Feature Screening Framework for Ultrahigh-Dimensional Survival Data. Mathematics. 2026; 14(1):93.
https://doi.org/10.3390/math14010093
Chicago/Turabian Style
Min, Xinyi, Wenbo Wu, and Baoying Yang.
2026. "A Two-Stage Feature Screening Framework for Ultrahigh-Dimensional Survival Data" Mathematics 14, no. 1: 93.
https://doi.org/10.3390/math14010093
APA Style
Min, X., Wu, W., & Yang, B.
(2026). A Two-Stage Feature Screening Framework for Ultrahigh-Dimensional Survival Data. Mathematics, 14(1), 93.
https://doi.org/10.3390/math14010093
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article Access Statistics
For more information on the journal statistics, click
here.
Multiple requests from the same IP address are counted as one view.
