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Related Experiment Video

Updated: May 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Variable Selection in Ultra-high Dimensional Feature Space for the Cox Model with Interval-Censored Data.

Daewoo Pak¹, Jianrui Zhang², Di Wu³

¹Division of Data Science, Yonsei University, Wonju 26493, Korea.

Journal of Statistical Planning and Inference

|May 21, 2026

Summary

This summary is machine-generated.

This study introduces variable selection methods for interval-censored data in ultra-high dimensions. These penalized Cox model approaches demonstrate strong performance and achieve the oracle property, proving effective in genetic studies.

Keywords:

Cox model interval censoring oracle property ultra-high dimension variable selection

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Area of Science:

Statistics
Genetics
Biostatistics

Background:

Ultra-high dimensional data presents challenges for traditional statistical models.
Interval censoring is common in survival analysis but complicates variable selection.
Cox proportional hazards models are widely used for survival data.

Purpose of the Study:

To develop and validate variable selection methods for Cox models with interval-censored data.
To address the challenges posed by ultra-high dimensional settings where predictors can grow exponentially.
To ensure selected variables are robust and reliable in complex datasets.

Main Methods:

Utilized penalized nonparametric maximum likelihood estimation.
Applied popular penalty functions: lasso, adaptive lasso, SCAD, and MCP.
Proved the oracle property for methods using folded concave or adaptive lasso penalties.

Main Results:

Proposed penalized variable selection methods demonstrate the oracle property.
Extensive simulations show satisfactory empirical performance across various scenarios.
The methods were successfully applied to a genome-wide association study.

Conclusions:

The developed variable selection methods are effective for interval-censored data in ultra-high dimensions.
These methods offer a robust approach for identifying relevant covariates in complex survival data.
The successful application highlights their utility in genetic association studies.