Feature screening in ultrahigh-dimensional additive Cox model

Guangren Yang, Sumin Hou, Luheng Wang, Yanqing Sun

Research output: Contribution to journalArticle

Abstract

The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes the actual active predictors. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. Furthermore, we examine the finite sample performance of the proposed procedure via Monte Carlo simulations, and illustrate the proposed procedure by a real data example.

Original languageEnglish (US)
Pages (from-to)1117-1133
Number of pages17
JournalJournal of Statistical Computation and Simulation
Volume88
Issue number6
DOIs
StatePublished - Apr 13 2018

Fingerprint

Cox Model
Additive Models
Screening
Predictors
Ascent
Survival Analysis
Regression Coefficient
Covariates
Monte Carlo Simulation
Cox model
Modeling

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

Yang, Guangren ; Hou, Sumin ; Wang, Luheng ; Sun, Yanqing. / Feature screening in ultrahigh-dimensional additive Cox model. In: Journal of Statistical Computation and Simulation. 2018 ; Vol. 88, No. 6. pp. 1117-1133.
@article{49da229516b44a3d9d1be8b57706eead,
title = "Feature screening in ultrahigh-dimensional additive Cox model",
abstract = "The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes the actual active predictors. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. Furthermore, we examine the finite sample performance of the proposed procedure via Monte Carlo simulations, and illustrate the proposed procedure by a real data example.",
author = "Guangren Yang and Sumin Hou and Luheng Wang and Yanqing Sun",
year = "2018",
month = "4",
day = "13",
doi = "10.1080/00949655.2017.1422127",
language = "English (US)",
volume = "88",
pages = "1117--1133",
journal = "Journal of Statistical Computation and Simulation",
issn = "0094-9655",
publisher = "Taylor and Francis Ltd.",
number = "6",

}

Feature screening in ultrahigh-dimensional additive Cox model. / Yang, Guangren; Hou, Sumin; Wang, Luheng; Sun, Yanqing.

In: Journal of Statistical Computation and Simulation, Vol. 88, No. 6, 13.04.2018, p. 1117-1133.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Feature screening in ultrahigh-dimensional additive Cox model

AU - Yang, Guangren

AU - Hou, Sumin

AU - Wang, Luheng

AU - Sun, Yanqing

PY - 2018/4/13

Y1 - 2018/4/13

N2 - The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes the actual active predictors. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. Furthermore, we examine the finite sample performance of the proposed procedure via Monte Carlo simulations, and illustrate the proposed procedure by a real data example.

AB - The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes the actual active predictors. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. Furthermore, we examine the finite sample performance of the proposed procedure via Monte Carlo simulations, and illustrate the proposed procedure by a real data example.

UR - http://www.scopus.com/inward/record.url?scp=85040975816&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040975816&partnerID=8YFLogxK

U2 - 10.1080/00949655.2017.1422127

DO - 10.1080/00949655.2017.1422127

M3 - Article

VL - 88

SP - 1117

EP - 1133

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

SN - 0094-9655

IS - 6

ER -