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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Feature Screening in Ultrahigh Dimensional Cox's Model.

Guangren Yang¹, Ye Yu², Runze Li³

¹School of Economics, Jinan University, Guangzhou, P.R. China.

Statistica Sinica

|July 16, 2016

Summary

This summary is machine-generated.

This study introduces a novel feature screening method for Cox models with ultrahigh dimensional data. The approach effectively identifies relevant genetic markers and predictors, improving survival data analysis.

Keywords:

and phrases: Cox's model partial likelihood penalized likelihood ultrahigh dimensional survival data

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Area of Science:

Statistics
Bioinformatics
Genomics

Background:

Medical studies increasingly collect survival data with ultrahigh dimensional covariates, such as genetic markers.
Analyzing this data requires effective methods to identify relevant predictors from a vast number of variables.

Purpose of the Study:

To propose a novel feature screening procedure for Cox models with ultrahigh dimensional covariates.
To identify active predictors that are jointly dependent but marginally independent of the response.

Main Methods:

A joint likelihood-based feature screening procedure is proposed, distinguishing it from marginal screening methods.
A computationally effective algorithm is developed to implement the procedure, with its ascent property established.
The sure screening property of the procedure is proven, ensuring selection of actual active predictors with high probability.

Main Results:

The proposed procedure effectively identifies jointly dependent predictors missed by marginal screening methods.
Monte Carlo simulations demonstrate the finite sample performance and superiority over existing Sure Independence Screening (SIS) procedures.
The methodology is validated through an empirical analysis of a real-world medical dataset.

Conclusions:

The proposed joint likelihood-based feature screening procedure is a powerful tool for analyzing ultrahigh dimensional survival data.
This method enhances the ability to identify relevant genetic markers and other predictors in complex datasets.
The procedure offers a computationally efficient and statistically robust approach for feature selection in survival analysis.