Independent screening for single-index hazard rate models with ultra-high dimensional features

05/17/2011
by   Anders Gorst-Rasmussen, et al.
0

In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that in the case of generalized linear models, independent screening may suffice to capture all relevant features with high probability, even in ultra-high dimension. It is unclear whether this formal sure screening property is attainable when the response is a right-censored survival time. We propose a computationally very efficient independent screening method for survival data which can be viewed as the natural survival equivalent of correlation screening. We state conditions under which the method admits the sure screening property within a general class of single-index hazard rate models with ultra-high dimensional features. An iterative variant is also described which combines screening with penalized regression in order to handle more complex feature covariance structures. The methods are evaluated through simulation studies and through application to a real gene expression dataset.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/21/2021

Bayesian iterative screening in ultra-high dimensional settings

Variable selection in ultra-high dimensional linear regression is often ...
research
06/15/2023

Conditional variable screening for ultra-high dimensional longitudinal data with time interactions

In recent years we have been able to gather large amounts of genomic dat...
research
01/10/2022

SMLE: An R Package for Joint Feature Screening in Ultrahigh-dimensional GLMs

The sparsity-restricted maximum likelihood estimator (SMLE) has received...
research
05/09/2023

High-dimensional Feature Screening for Nonlinear Associations With Survival Outcome Using Restricted Mean Survival Time

Feature screening is an important tool in analyzing ultrahigh-dimensiona...
research
06/21/2022

BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space

The lack of freely available (real-life or synthetic) high or ultra-high...
research
02/17/2010

High-dimensional variable selection for Cox's proportional hazards model

Variable selection in high dimensional space has challenged many contemp...
research
09/21/2021

A Model-free Variable Screening Method Based on Leverage Score

With rapid advances in information technology, massive datasets are coll...

Please sign up or login with your details

Forgot password? Click here to reset